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1

Survey of Mathematical Models for Economic Forecasting.  

National Technical Information Service (NTIS)

Much research has been done in recent years in forecasting economic development and long-term planning of the national economy on the basis of mathematical models. An analysis of a number of works as well as of materials of scientific conferences and meet...

A. Tretyakova

1970-01-01

2

MASTER: a forecast model of reginal economic activity. [Metropolitan and state economic regions  

Microsoft Academic Search

The Metropolitan and State Economic Regions (MASTER) model is presented. This model was constructed by researchers at Pacific Northwest Laboratory in response to the need for a national multiregion model of economic activity in substate areas which can be used for forecasting, planning, and policymaking in energy-related fields. MASTER forecasts economic activity in all 268 Standard Metropolitan Statistical Areas (SMSAs)

R. J. Moe; R. C. Adams; M. J. Scott

1983-01-01

3

A Linear Forecasting Model and Its Application to Economic Data  

Microsoft Academic Search

We present a forecasting model based on fuzzy pattern recognition and weighted linear regression. In this model fuzzy pattern recognition is used to find homogeneous fuzzy classes in a heterogeneous data set. It is assumed that the classes represent typical situations. For each class a weighted regression analysis is conducted. The forecasting results obtained by the class regression analysis are

Georg Peters

2001-01-01

4

Publishing FOMC economic forecasts  

Microsoft Academic Search

Given the time lag between a monetary policy action and its effect on the economy, the importance of considering economic forecasts in the conduct of policy has long been acknowledged. Still, it is only over the past decade or so that the publication of central bank economic forecasts has been widely recognized as a potentially useful tool for monetary policy

Glenn D. Rudebusch

2008-01-01

5

Design of a multiregional economic model for forecasting electricity consumption and peak load. Final report  

SciTech Connect

The design of a general multiregional econometric model of the USA and the design of a regional electricity consumption and demand submodel are presented. The multiregional econometric model is intended to provide forecasts of regional population, economic activity by industrial sector, regional wages, and incomes. The electricity submodel is designed to take forecasts of such general economic indicators (together with forecasts of relative electricity and othe energy costs) and to produce forecasts of electricity (kWh) consumption by customer category and forecasts of peak load. While the ultimate purpose of the present effort is regional electricity forecasting, it is clear that the multiregional econometric model which supports the electricity submodel has a great many other uses. The multiregional econometric model design presented in the document represents a natural extension to the regional level of the Wharton Long-Term Annual and Industry Forecasting Model of the USA. The parts of that model that lend themselves to regional disaggregation (employment and wages, for example) are disaggregated. Aggregate US forecasts for such variables are determined by adding up from the bottom. This bottom-up design marks a major departure from earlier regional efforts. In addition to providing a description of the theoretical design of the model, this document provides an extensive review and evaluation of the economic and electricity-energy database needed for its construction.

Adams, F.G.; McCarthy, M.D.; Hill, J.

1982-01-01

6

Integration of Weather Research Forecast (WRF) Hurricane model with socio-economic data in an interactive web mapping service  

Microsoft Academic Search

The integration of weather forecast models and socio-economic data is key to better understanding of the weather forecast and its impact upon society. Whether the forecast is looking at a hurricane approaching land or a snow storm over an urban corridor; the public is most interested in how this weather will affect day-to-day activities, and in extreme events how it

J. Boehnert; O. Wilhelmi; K. M. Sampson

2009-01-01

7

Forecasting Economic Data with Neural Networks  

Microsoft Academic Search

Studies in recent years have attempted to forecast macroeconomic phenomena with neural networks reporting mixed results. This work represents an investigation of this problem using U.S. Real Gross Domestic Production and Industrial Production as case studies. This work is based on a coefficient of determination which accurately measures the ability of linear or nonlinear models to forecast economic data. The

Farzan Aminian; E. Dante Suarez; Mehran Aminian; Daniel T. Walz

2006-01-01

8

Safe-economical route and its assessment model of a ship to avoid tropical cyclones using dynamic forecast environment  

NASA Astrophysics Data System (ADS)

In heavy sea conditions related to tropical cyclones (TCs), losses to shipping caused by capsizing are greater than other kinds of accidents. Therefore, it is important to consider capsizing risk in the algorithms used to generate safe-economic routes that avoid tropical cyclones (RATC). A safe-economic routing and assessment model for RATC, based on a dynamic forecasting environment, is presented in this paper. In the proposed model, a ship's risk is quantified using its capsizing probability caused by heavy wave conditions. Forecasting errors in the numerical models are considered according to their distribution characteristics. A case study shows that: the economic cost of RATCs is associated not only to the ship's speed and the acceptable risk level, but also to the ship's wind and wave resistance. Case study results demonstrate that the optimal routes obtained from the model proposed in this paper are significantly superior to those produced by traditional methods.

Wu, L. C.; Wen, Y. Q.; Wu, D. Y.

2013-05-01

9

An empirical investigation on the forecasting ability of mallows model averaging in a macro economic environment  

NASA Astrophysics Data System (ADS)

This paper investigates the forecasting ability of Mallows Model Averaging (MMA) by conducting an empirical analysis of five Asia countries, Malaysia, Thailand, Philippines, Indonesia and China's GDP growth rate. Results reveal that MMA has no noticeable differences in predictive ability compared to the general autoregressive fractional integrated moving average model (ARFIMA) and its predictive ability is sensitive to the effect of financial crisis. MMA could be an alternative forecasting method for samples without recent outliers such as financial crisis.

Yin, Yip Chee; Hock-Eam, Lim

2012-09-01

10

A Small Global Forecasting Model  

Microsoft Academic Search

This paper describes the OECD’s new small global forecasting model for the three main OECD economic regions: the United States, the euro area, and Japan. The key variables – which include output, inflation, the trade balance, and import prices – are driven by monetary and fiscal policy, exchange rates, and world demand. The projections from the model are used as

David Rae; David Turner

2001-01-01

11

Aggregate vehicle travel forecasting model  

SciTech Connect

This report describes a model for forecasting total US highway travel by all vehicle types, and its implementation in the form of a personal computer program. The model comprises a short-run, econometrically-based module for forecasting through the year 2000, as well as a structural, scenario-based longer term module for forecasting through 2030. The short-term module is driven primarily by economic variables. It includes a detailed vehicle stock model and permits the estimation of fuel use as well as vehicle travel. The longer-tenn module depends on demographic factors to a greater extent, but also on trends in key parameters such as vehicle load factors, and the dematerialization of GNP. Both passenger and freight vehicle movements are accounted for in both modules. The model has been implemented as a compiled program in the Fox-Pro database management system operating in the Windows environment.

Greene, D.L.; Chin, Shih-Miao; Gibson, R. [Tennessee Univ., Knoxville, TN (United States)

1995-05-01

12

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators  

PubMed Central

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

2013-01-01

13

California 2008-2030 County-Level Economic Forecast.  

National Technical Information Service (NTIS)

Economic forecasts for the 2008 to 2030 period have been prepared for each county of California. A forecast for the entire state has also been developed and included. The forecasts utilize the most recent historical information through calendar 2007, avai...

M. Schniepp

2008-01-01

14

Forecasting model of gaming revenues in Clark County, Nevada.  

National Technical Information Service (NTIS)

This paper describes the Western Area Gaining and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, w...

B. Edwards A. Bando G. Bassett A. Rosen J. Carlson

1992-01-01

15

Forecasting and uncertainty in the economic and business world  

Microsoft Academic Search

Forecasts are crucial for practically all economic and business decisions. However, there is a mounting body of empirical evidence showing that accurate forecasting in the economic and business world is usually not possible. In addition, there is huge uncertainty, as practically all economic and business activities are subject to events we are unable to predict. The fact that forecasts can

Spyros Makridakis; Robin M. Hogarth; Anil Gaba

2009-01-01

16

Methodological Problems in Forecasting Economic Development and Technical Progress  

Microsoft Academic Search

Under socialism, scientific forecasting is a necessary stage in the process of national economic planning. It is called upon to determine, in the stage preceding the plan, possible variants of technical, economic, and social development in the long run, the knowledge of which makes it possible to proceed to the preparation of state national economic plans. In the forecasting process,

L. Berri

1970-01-01

17

California 2011-2040 County-Level Economic Forecast.  

National Technical Information Service (NTIS)

The 2011 county-level long term forecast for all 58 counties of California is presented in this edition of the CalTrans Economic Forecast. The forecast was conducted from June 2011 through August 2011. Actual information for the state, the nation and the ...

M. Schniepp

2011-01-01

18

California 2010-2035 County-Level Economic Forecast.  

National Technical Information Service (NTIS)

The 2010 county-level long term forecast for all 58 counties of California is presented in this edition of the Caltrans Economic Forecast. The forecast was conducted from November 2009 through February 2010. Actual information for the state, the nation an...

M. Schniepp

2010-01-01

19

California 2012-2040 County-Level Economic Forecast.  

National Technical Information Service (NTIS)

The 2012 county-level long term forecast for all 58 counties of California is presented in this edition of the CalTrans Economic Forecast. The forecast was conducted from June 2012 through September 2012. Actual information for the state, the nation and t...

M. Schniepp

2012-01-01

20

Ups and downs of economics and econophysics — Facebook forecast  

NASA Astrophysics Data System (ADS)

What is econophysics and its relationship with economics? What is the state of economics after the global economic crisis, and is there a future for the paradigm of market equilibrium, with imaginary perfect competition and rational agents? Can the next paradigm of economics adopt important assumptions derived from econophysics models: that markets are chaotic systems, striving to extremes as bubbles and crashes show, with psychologically motivated, statistically predictable individual behaviors? Is the future of econophysics, as predicted here, to disappear and become a part of economics? A good test of the current state of econophysics and its methods is the valuation of Facebook immediately after the initial public offering — this forecast indicates that Facebook is highly overvalued, and its IPO valuation of 104 billion dollars is mostly the new financial bubble based on the expectations of unlimited growth, although it's easy to prove that Facebook is close to the upper limit of its users.

Gajic, Nenad; Budinski-Petkovic, Ljuba

2013-01-01

21

Comparison of Wind energy production forecasts, in terms of errors and economic losses  

NASA Astrophysics Data System (ADS)

We compare 6 forecasts productions models on two windfarms located in France. The evaluation is made in terms of root mean square errors. The power production forecasts are the products of both physical and statistical models and cover a period of 6 months. We show that the economic performances of those models can be improved using econometric approaches, where we to minimize the cost induced by the forecast error instead of minimizing the forecast error itself. This technique relies on state of the art non-parametric estimators of conditional probability distribution functions (cpdf) of energy production at a wind farm, given the wind speed forecasts of a deterministic meteorological model. In this case, no assumption is made about the shape of the underlying laws. The economical benefits of ensemble versus deterministic wind speed forecasts are also assessed.

Mestre, O.; Texier, O.; Girard, N.; Usaola, J.; Bantegnie, P.

2009-04-01

22

Decision Making Under Uncertainty: Economic Evaluation of Streamflow Forecasts.  

National Technical Information Service (NTIS)

The research focuses on the potential economic benefits that could accrue to different classes of water users as a result of improvements in the accuracy of streamflow forecasts. Detailed efforts for the evaluation of the economic benefits from improved s...

G. Schramm R. W. Fenton J. L. Moore D. Hughart G. R. Moore

1974-01-01

23

How to Use Econometric Models to Forecast.  

National Technical Information Service (NTIS)

The purpose of this paper is to describe the procedures followed by the Research Department of the Federal Reserve Bank of Minneapolis in producing a forecast of natural economic activity with the aid of a large econometric model. We produce such a foreca...

T. M. Supel

1975-01-01

24

The effects of age structure on economic growth: An application of probabilistic forecasting to India  

Microsoft Academic Search

During recent years there has been an increasing awareness of the explanatory power of population age structure variables in economic growth regressions. We estimate a new cross-country regression model of the effects of age structure change on economic growth. We use the new model and recent probabilistic demographic forecasts for India to derive the uncertainty of predicted economic growth rates

Alexia Prskawetz; T. Kögel; Warren C. Sanderson; Sergei Scherbov

2007-01-01

25

The economic benefit of short-term forecasting for wind energy in the UK electricity market  

Microsoft Academic Search

In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind

R. J. Barthelmie; F. Murray; S. C. Pryor

2008-01-01

26

AVLIS: a technical and economic forecast  

SciTech Connect

The AVLIS process has intrinsically large isotopic selectivity and hence high separative capacity per module. The critical components essential to achieving the high production rates represent a small fraction (approx.10%) of the total capital cost of a production facility, and the reference production designs are based on frequent replacement of these components. The specifications for replacement frequencies in a plant are conservative with respect to our expectations; it is reasonable to expect that, as the plant is operated, the specifications will be exceeded and production costs will continue to fall. Major improvements in separator production rates and laser system efficiencies (approx.power) are expected to occur as a natural evolution in component improvements. With respect to the reference design, such improvements have only marginal economic value, but given the exigencies of moving from engineering demonstration to production operations, we continue to pursue these improvements in order to offset any unforeseen cost increases. Thus, our technical and economic forecasts for the AVLIS process remain very positive. The near-term challenge is to obtain stable funding and a commitment to bring the process to full production conditions within the next five years. If the funding and commitment are not maintained, the team will disperse and the know-how will be lost before it can be translated into production operations. The motivation to preserve the option for low-cost AVLIS SWU production is integrally tied to the motivation to maintain a competitive nuclear option. The US industry can certainly survive without AVLIS, but our tradition as technology leader in the industry will certainly be lost.

Davis, J.I.; Spaeth, M.L.

1986-01-01

27

Forecasting the Future Economic Burden of Current Adolescent Overweight: An Estimate of the Coronary Heart Disease Policy Model  

PubMed Central

Objectives. We predicted the future economic burden attributable to high rates of current adolescent overweight. Methods. We constructed models to simulate the costs of excess obesity and associated diabetes and coronary heart disease (CHD) among adults aged 35–64 years in the US population in 2020 to 2050. Results. Current adolescent overweight is projected to result in 161 million life-years complicated by obesity, diabetes, or CHD and 1.5 million life-years lost. The cumulative excess attributable total costs are estimated at $254 billion: $208 billion because of lost productivity from earlier death or morbidity and $46 billion from direct medical costs. Currently available therapies for hypertension, hyperlipidemia, and diabetes, used according to guidelines, if applied in the future, would result in modest reductions in excess mortality (decreased to 1.1 million life-years lost) but increase total excess costs by another $7 billion (increased to $261 billion total). Conclusions. Current adolescent overweight will likely lead to large future economic and health burdens, especially lost productivity from premature death and disability. Application of currently available medical treatments will not greatly reduce these future burdens of increased adult obesity.

Bibbins-Domingo, Kirsten; Coxson, Pamela; Wang, Y. Claire; Williams, Lawrence; Goldman, Lee

2009-01-01

28

Bias in OMB's economic forecasts and budget proposals  

Microsoft Academic Search

The Office of Management and Budget (OMB) annually submits the President's budget for the U.S. government to the Congress. The economic forecasts and revenue and outlay proposals contained in the budget have been criticized as biased, especially during the 1980s. Tests for bias in one year ahead proposals for the 1963–89 period show no bias in economic forecasts and revenue

Paul R. Blackley; Larry DeBoer

1993-01-01

29

Forecasting state retail sales: Econometric vs. time series models  

Microsoft Academic Search

The volume of retail sales is an important indicator of state economic activity and forms the base of the percentage sales tax. Accurate forecasting of the variable is of interest to fiscal authorities and private analysts as well. This paper compares the performance of two techniques in forecasting net taxable retail sales: the ARIMA time series model and a structural

James R. Schmidt

1979-01-01

30

FORECASTING INTERNATIONAL MIGRATION: SELECTED THEORIES, MODELS, AND METHODS  

Microsoft Academic Search

The paper attempts to outline selected theoretical fundamentals of international migration forecasting. In general, socio-economic predictions can be based either on general laws and theories, or on models designed to suit specific research questions. The discussion offers a brief overview of selected migration theories, as well as a short evaluation of their applicability in forecasting international population flows. Subsequently, a

Jakub Bijak

31

Forecasting industrial motor stock: Economic theory and policy analysis  

Microsoft Academic Search

The State Utility Forecasting Group (SUFG) at Purdue University utilizes econometric and end-use models to develop forecasts of electricity prices and demand on a timely basis for the Indiana Utility Regulatory Commission, in compliance with Indiana Code 8-1-8.5. As part of this procedure, SUFG must regularly update the forecast methodology.^ An end-use model of industrial electricity demand was developed to

Kevin Louis Stamber

1998-01-01

32

A forecasting model of gaming revenues in Clark County, Nevada  

SciTech Connect

This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry, an identify the exogenous variables that affect gaming revenues. This model will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming related economic activity resulting from future events like the siting of a permanent high-level radioactive waste repository at Yucca Mountain.

Edwards, B.; Bando, A.; Bassett, G.; Rosen, A. [Argonne National Lab., IL (United States); Carlson, J.; Meenan, C. [Science Applications International Corp., Las Vegas, NV (United States)

1992-04-01

33

Economic indicators selection for crime rates forecasting using cooperative feature selection  

NASA Astrophysics Data System (ADS)

Features selection in multivariate forecasting model is very important to ensure that the model is accurate. The purpose of this study is to apply the Cooperative Feature Selection method for features selection. The features are economic indicators that will be used in crime rate forecasting model. The Cooperative Feature Selection combines grey relational analysis and artificial neural network to establish a cooperative model that can rank and select the significant economic indicators. Grey relational analysis is used to select the best data series to represent each economic indicator and is also used to rank the economic indicators according to its importance to the crime rate. After that, the artificial neural network is used to select the significant economic indicators for forecasting the crime rates. In this study, we used economic indicators of unemployment rate, consumer price index, gross domestic product and consumer sentiment index, as well as data rates of property crime and violent crime for the United States. Levenberg-Marquardt neural network is used in this study. From our experiments, we found that consumer price index is an important economic indicator that has a significant influence on the violent crime rate. While for property crime rate, the gross domestic product, unemployment rate and consumer price index are the influential economic indicators. The Cooperative Feature Selection is also found to produce smaller errors as compared to Multiple Linear Regression in forecasting property and violent crime rates.

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Salleh Sallehuddin, Roselina

2013-04-01

34

Forecasting Model of the PEPCO Service Area Economy. Volume 3.  

National Technical Information Service (NTIS)

Volume III describes and documents the regional economic model of the PEPCO service area which was relied upon to develop many of the assumptions of future values of economic and demographic variables used in the forecast. The PEPCO area model is mathemat...

1984-01-01

35

A forecasting metric for predictive modeling  

Microsoft Academic Search

In science and engineering, simulation models calibrated against a limited number of experiments are commonly used to forecast at settings where experiments are unavailable, raising concerns about the unknown forecasting errors. Forecasting errors can be quantified and controlled by deploying statistical inference procedures, combined with an experimental campaign to improve the fidelity of a simulation model that is developed based

Sez Atamturktur; François Hemez; Brian Williams; Carlos Tome; Cetin Unal

2011-01-01

36

A Forecast Model of the Employment Rate in Romania  

Microsoft Academic Search

The paper analyzes the dynamics of the population employment rate in Romania and identifies a model of its forecast. The evaluation of the dynamics of the employment rate and of the influence factors - demographic, economic, demo-economic - is made analyzing the indexes of the dynamics. The analysis of the dynamics employment rate correlated with the dynamics of the influence

Elisabeta JABA; Carmen PINTILESCU; Elena Daniela VIORICA; Christiana Brigitte BALAN

37

Bridge models to forecast the euro area GDP  

Microsoft Academic Search

Quantitative information on the current state of the economy is crucial to economic policy-making and to early understanding of the economic situation, but the quarterly national account (NA) data for GDP in the euro area are released with a substantial delay. The aim of the paper is to examine the forecast ability of bridge models (BM) for GDP growth in

Alberto Baffigi; Roberto Golinelli; Giuseppe Parigi

2004-01-01

38

Statistical Post-Processing of Wind Speed Forecasts to Estimate Relative Economic Value  

NASA Astrophysics Data System (ADS)

The objective of this research is to get the best possible wind speed forecasts for the wind energy industry by using an optimal combination of well-established forecasting and post-processing methods. We start with the ECMWF 51 member ensemble prediction system (EPS) which is underdispersive and hence uncalibrated. We aim to produce wind speed forecasts that are more accurate and calibrated than the EPS. The 51 members of the EPS are clustered to 8 weighted representative members (RMs), chosen to minimize the within-cluster spread, while maximizing the inter-cluster spread. The forecasts are then downscaled using two limited area models, WRF and COSMO, at two resolutions, 14km and 3km. This process creates four distinguishable ensembles which are used as input to statistical post-processes requiring multi-model forecasts. Two such processes are presented here. The first, Bayesian Model Averaging, has been proven to provide more calibrated and accurate wind speed forecasts than the ECMWF EPS using this multi-model input data. The second, heteroscedastic censored regression is indicating positive results also. We compare the two post-processing methods, applied to a year of hindcast wind speed data around Ireland, using an array of deterministic and probabilistic verification techniques, such as MAE, CRPS, probability transform integrals and verification rank histograms, to show which method provides the most accurate and calibrated forecasts. However, the value of a forecast to an end-user cannot be fully quantified by just the accuracy and calibration measurements mentioned, as the relationship between skill and value is complex. Capturing the full potential of the forecast benefits also requires detailed knowledge of the end-users' weather sensitive decision-making processes and most importantly the economic impact it will have on their income. Finally, we present the continuous relative economic value of both post-processing methods to identify which is more beneficial to the wind energy industry of Ireland.

Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

2013-04-01

39

Economic Perspectives of Technological Progress: New Dimensions for Forecasting Technology  

ERIC Educational Resources Information Center

Discusses the causal relationship between the allocation of financial resources and technological growth. Argues that economic constraints are becoming an important determinant of technological progress that must be incorporated into technology forecasting techniques. (Available from IPC (America) Inc., 205 East 42 Street, New York, NY 10017;…

Twiss, Brian

1976-01-01

40

Study to Develop Statewide and County-Level Economic Projections. Volume 1. California County Economic Forecasts.  

National Technical Information Service (NTIS)

Volume I provides a description of forecasts of economic growth for California and its large metropolitan areas. The California economy will continue to lag the national economy until 1996. In the longer term, California will surpass the nation in growth ...

J. Kloepfer

1994-01-01

41

On the Economic Value of Seasonal-Precipitation Forecasts: The Fallowing/Planting Problem.  

NASA Astrophysics Data System (ADS)

The so-called fallowing/planting problem is an example of a decision-making situation that is potentially sensitive to meteorological information. In this problem, wheat farmers in the drier, western portions of the northern Great Plains must decide each spring whether to plant a crop or to let their land lie fallow. Information that could be used to make this decision includes the soil moisture at planting time and a forecast of growing-season precipitation. A dynamic decision-making model is employed to investigate the economic value of such forecasts in the fallowing/planting situation.Current seasonal-precipitation forecasts issued by the National Weather Service are found to have minimal economic value in this decision-making problem. However, relatively modest improvements in the quality of the forecasts would lead to quite large increases in value, and perfect information would possess considerable value. In addition, forecast value is found to be sensitive to changes in crop price and precipitation climatology. In particular, the shape of the curve relating forecast value to forecast quality is quite dependent on the amount of growing-season precipitation.

Brown, Barbara G.; Katz, Richard W.; Murphy, Allan H.

1986-07-01

42

Forecasting Seven Components of the Food CPI: An Initial Assessment. An Economic Research Service Report.  

National Technical Information Service (NTIS)

The purpose of this report is to make the price forecasting procedure used by the Food and Consumer Economics Division of the Economic Research Service transparent to users, and to evaluate the quality of the forecasts. After documenting and interpreting ...

M. Denbaly C. Hallahan F. Joutz A. Reed R. Trost

1996-01-01

43

Economic impact of wind power forecast  

Microsoft Academic Search

1. Abstract Prediction tools can make wind energy be competitive in a liberalized energy market, where deviations in production have a penalty which is usually an obstacle for developers to access to the energy market. This paper analyses these deviations and the economic impact over any developer according to the actual pricing policy. This is made through simulations based on

I. Marti; M. J. San Isidro; M. Gastón; Y. Loureiro; J. Sanz; I. Pérez

44

Construction industry forecasting system dynamic model  

Microsoft Academic Search

In a paper construction branch forecasting model which allows to estimate the industry development problems is shown. Difference from anthers models, in given paper the main attention is turned to the building of the living area. Model stands from sub model (blocks): amount of apartments, real estate prices, necessity of apartments and living area forecasting models. Their essence and necessity

Valerijs Skribans

2010-01-01

45

Identifying Effects of Forecast Uncertainty on Flood Control Decision - A Hydro-economic Hedging Framework  

NASA Astrophysics Data System (ADS)

Different from conventional studies developing reservoir operation models and treating forecast as input to obtain operation decisions case by case, this study issues a hydro-economic analysis framework and derives some general relationships between optimal flood control decision and streamflow forecast. By analogy with the hedging rule theory for water supply, we formulate reservoir flood control with a two-stage optimization model, in which the properties of flood damage (i.e., diminishing marginal damage) and the characteristics of forecast uncertainty (i.e., the longer the forecast horizon, the larger the forecast uncertainty) are incorporated to minimize flood risk. We define flood conveying capacity surplus (FCCS) variables to elaborate the trade-offs between the release of current stage (i.e., stage 1) and in the release of future stage (i.e., stage 2). Using Karush-Kuhn-Tucker conditions, the flood risk trade-off between the two stages is theoretically represented and illustrated by three typical situations depending on forecast uncertainty and flood magnitude. The analytical results also show some complicated effects of forecast uncertainty and flood magnitude on real-time flood control decision: 1) When there is a big flood with a small FCCS, the whole FCCS should be allocated to the current stage to hedge against the more certain and urgent flood risk in the current stage; 2) when there is a medium flood with a moderate FCCS, some FCCS should be allocated to the future stage but more FCCS still should be allocated to the current stage; and 3) when there is a small flood with a large FCCS, more FCCS should be allocated to the future stage than the current stage, as a large FCCS in the future stage can still induce some flood risk (distribution of future stage forecast uncertainty is more disperse) while a moderate FCCS in the current stage can induce a small risk. Moreover, this study also presents a hypothetical case study to analyze the flood risk under Pseudo probabilistic streamflow forecast (pPSF, deterministic forecast with variance) and Real probabilistic streamflow forecast (rPSF, ensemble forecast) forecast uncertainties, which shows ensemble forecast techniques are more efficient on mitigating flood risk.

Zhao, T.; Zhao, J.; Cai, X.; Yang, D.

2011-12-01

46

A Baseline Economic Forecast of the U.S. Fishing Industry. Volume I. A Baseline Economic Forecast of the U.S. Fishing Industry: 1974-1985. Volume II. Economic Forecast Database. Volume III. Computer Documentation. Volume IV. Technical Appendices.  

National Technical Information Service (NTIS)

The following report is one of two economic studies prepared for the National Marine Fisheries Service (NMFS) developed as background information for the National Plan for Marine Fisheries. Presented is a baseline forecast of the economic characteristics ...

N. Williams T. R. Colvin D. L. Zimmerman

1974-01-01

47

A fuzzy seasonal ARIMA model for forecasting  

Microsoft Academic Search

This paper proposes a fuzzy seasonal ARIMA (FSARIMA) forecasting model, which combines the advantages of the seasonal time series ARIMA (SARIMA) model and the fuzzy regression model. It is used to forecast two seasonal time series data of the total production value of the Taiwan machinery industry and the soft drink time series. The intention of this paper is to

Fang-mei Tseng; Gwo-hshiung Tzeng

2002-01-01

48

Operational, regional-scale, chemical weather forecasting models in Europe  

Microsoft Academic Search

Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical

J. Kukkonen; T. Balk; D. M. Schultz; A. Baklanov; T. Klein; A. I. Miranda; A. Monteiro; M. Hirtl; V. Tarvainen; M. Boy; V.-H. Peuch; A. Poupkou; I. Kioutsioukis; S. Finardi; M. Sofiev; R. Sokhi; K. Lehtinen; K. Karatzas; M. Astitha; G. Kallos; M. Schaap; E. Reimer; H. Jakobs; K. Eben

2011-01-01

49

How to Support a One-Handed Economist: The Role of Modalisation in Economic Forecasting  

ERIC Educational Resources Information Center

|Economic forecasting in the world of international finance confronts economists with challenging cross-cultural writing tasks. Producing forecasts in English which convey confidence and credibility entails an understanding of linguistic conventions which typify the genre. A typical linguistic feature of commercial economic forecasts produced by…

Donohue, James P.

2006-01-01

50

Description of the Battlescale Forecast Model.  

National Technical Information Service (NTIS)

The Battlescale Forecast Model, which was developed at the U.S. Army Research Laboratory, is a major part of the U.S. Army Integrated Meteorological System Block II software. The Battlescale Forecast Model can be used operationally over any part of the wo...

T. Henmi R. Dumais

1998-01-01

51

Forecasting Irish inflation using ARIMA models  

Microsoft Academic Search

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. The emphasis is on

Aidan Meyler; Terry Quinn

1998-01-01

52

Essays on forecasting stationary and nonstationary economic time series  

NASA Astrophysics Data System (ADS)

This dissertation consists of three essays. Chapter II considers the question of whether M2 growth can be used to forecast inflation at horizons of up to ten years. A vector error correction (VEC) model serves as our benchmark model. We find that M2 growth does have marginal predictive content for inflation at horizons of more than two years, but only when allowing for cointegration and when the cointegrating rank and vector are specified a priori. When estimating the cointegration vector or failing to impose cointegration, there is no longer evidence of causality running from M2 growth to inflation at any forecast horizon. Finally, we present evidence that M2 needs to be redefined, as forecasts of the VEC model using data on M2 observed after 1993 are worse than the forecasts of an autoregressive model of inflation. Chapter III reconsiders the evidence for a "rockets and feathers" effect in gasoline markets. We estimate an error correction model of gasoline prices using daily data for the period 1985--1998 and fail to find any evidence of asymmetry. We show that previous work suffered from two problems. First, nonstationarity in some of the regressors was ignored, leading to invalid inference. Second, the weekly data used in previous work leads to a temporal aggregation problem, and thus biased estimates of impulse response functions. Chapter IV tests for a forecasting relationship between the volume of litigation and macroeconomic variables. We analyze annual data for the period 1960--2000 on the number of cases filed, real GDP, real consumption expenditures, inflation, unemployment, and interest rates. Bivariate Granger causality tests show that several of the macroeconomic variables can be used to forecast the volume of litigation, but show no evidence that the volume of litigation can be used to forecast any of the macroeconomic variables. The analysis is then extended to bivariate and multivariate regression models, and we find similar evidence to that of the Granger causality tests. We conclude that agents desiring a forecast of the volume of litigation should consider the state of the economy.

Bachmeier, Lance Joseph

53

A univariate model for long-term streamflow forecasting  

Microsoft Academic Search

This paper, the first in a series of two, employs the principle of maximum entropy (POME) via maximum entropy spectral analysis (MESA) to develop a univariate model for long-term streamflow forecasting. Three cases of streamflow forecasting are investigated: forward forecasting, backward forecasting (or reconstruction) and intermittent forecasting (or filling in missing records). Application of the model is discussed in the

P. F. Krstanovic; V. P. Singh

1991-01-01

54

AIR QUALITY MODEL EVALUATION - FORECASTING AND RETROSPECTIVES  

EPA Science Inventory

This presentation discusses the CMAQ model evaluation framework, and presents results of evaluation of CMAQ's particulate matter estimates for PM2.5, and its components for 2005 air quality forecast predictions as well as retrospective modeling for 2001....

55

Stability and Forecasting in a Chaotic Environmental-Economic System.  

National Technical Information Service (NTIS)

The basic interactions of the computerized environmental-economic model are presented. The included interactions endeavour to cause the permanence, temporariness and irreversibility of the macroscopic environmental-economic processes. The system variables...

H. M. Isomaeki S. P. Kantola

1998-01-01

56

Nambe Pueblo Water Budget and Forecasting model.  

SciTech Connect

This report documents The Nambe Pueblo Water Budget and Water Forecasting model. The model has been constructed using Powersim Studio (PS), a software package designed to investigate complex systems where flows and accumulations are central to the system. Here PS has been used as a platform for modeling various aspects of Nambe Pueblo's current and future water use. The model contains three major components, the Water Forecast Component, Irrigation Scheduling Component, and the Reservoir Model Component. In each of the components, the user can change variables to investigate the impacts of water management scenarios on future water use. The Water Forecast Component includes forecasting for industrial, commercial, and livestock use. Domestic demand is also forecasted based on user specified current population, population growth rates, and per capita water consumption. Irrigation efficiencies are quantified in the Irrigated Agriculture component using critical information concerning diversion rates, acreages, ditch dimensions and seepage rates. Results from this section are used in the Water Demand Forecast, Irrigation Scheduling, and the Reservoir Model components. The Reservoir Component contains two sections, (1) Storage and Inflow Accumulations by Categories and (2) Release, Diversion and Shortages. Results from both sections are derived from the calibrated Nambe Reservoir model where historic, pre-dam or above dam USGS stream flow data is fed into the model and releases are calculated.

Brainard, James Robert

2009-10-01

57

Alternative P* Models of Inflation Forecasts  

Microsoft Academic Search

This paper reevaluates the inflation forecast performance of M2-based P* models relative to other competing models over the period of 1970-96. Included in the comparative study are newly developed monetary aggregates, including M2+, MZM, and M2*, and direct treatments of velocity changes associated with recent developments in M2. Out-of-sample rolling-horizon forecast exercises suggest that the predictive accuracy of alternative P*

Jim Lee

1999-01-01

58

Forecasting commercial rental values using ARIMA models  

Microsoft Academic Search

The application of short-term forecasting techniques to the prediction of commercial rental values generates valuable information about the dynamics of rent movements. It also captures short-run trends more effectively than do other forecasting procedures. Makes use of ARIMA models to provide one-step-ahead predictions. The results show that ARIMA models perform better in the case of retail and office sectors. The

Tony McGough; Sotiris Tsolacos

1995-01-01

59

A Forecast Method of Economic Data and Its Application  

Microsoft Academic Search

In this paper, a economic data analysis model is been proposed. There are three step in the mode. First, the economics data is decomposed by wavelet analysis. Second, some of the data is combined according to the cycle of the data. At last. The ARIMA model is been used to analysis.

Bin-sheng Liu; Zhi-jian Wang

2009-01-01

60

LOGISTIC SUBSTITUTION MODEL AND TECHNOLOGICAL FORECASTING  

Microsoft Academic Search

In this paper the application of several models, based on the logistic growth function (simple logistic, component logistic and logistic substitution models) in the context of technology change forecasting is discussed. The main idea of this paper is to revise existing models and arrange working hypotheses for future research. First, the features of a simple logistic model are presented, different

Dmitry Kucharavy; Roland De Guio

2008-01-01

61

Forecasting State Government Revenues in Arizona: Some Economic Planning Elements.  

National Technical Information Service (NTIS)

Success in forecasting state tax revenues is dependent upon an ability to forecast changes in the State's major tax revenue bases: income, property value, motor vehicles, and sales. Arizonas economy is closely related to and responsive to the national eco...

R. D. Beeman

1971-01-01

62

Influence of Model Physics on NWP Forecasts  

NSDL National Science Digital Library

This module describes model parameterizations of sub-surface, boundary-layer,and free atmospheric processes, such as surface snow processes, soil characteristics, vegetation, evapotranspiration, PBL processes and parameterizations, and trace gases, and their interaction with the radiative transfer process. It specifically addresses how models treat these physical processes and how they can influence forecasts of sensible weather elements.

Spangler, Tim

1999-11-24

63

Numerical model for quantitative precipitation forecasting  

Microsoft Academic Search

A numerical model for the quantitative precipitation forecasting has been formulated. In this model precipitation is computed as a function of the vertical velocity and humidity distribution in the atmosphere. The orographic influence on the vertical velocity was taken into consideration. Further, the relation between vertical velocity and static stability of the atmosphere has been considered and, as an important

Djuro Radinovic

1972-01-01

64

Forecasting River Uruguay flow using rainfall forecasts from a regional weather-prediction model  

Microsoft Academic Search

The use of quantitative rainfall forecasts as input to a rainfall-runoff model, thereby extending the lead-time of flow forecasts, is relatively new. This paper presents results from a study in which real-time river flow forecasts were calculated for the River Uruguay basin lying within southern Brazil, using a method based on observed rainfall, quantitative forecasts of rainfall given by a

Walter Collischonn; Reinaldo Haas; Ivanilto Andreolli; Carlos Eduardo Morelli Tucci

2005-01-01

65

Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting  

NASA Astrophysics Data System (ADS)

Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.

Robertson, D. E.; Shrestha, D. L.; Wang, Q. J.

2013-05-01

66

Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting  

NASA Astrophysics Data System (ADS)

Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.

Robertson, D. E.; Shrestha, D. L.; Wang, Q. J.

2013-09-01

67

A Forecasting Metric for Predictive Modeling  

Microsoft Academic Search

\\u000a As the complexity of engineering systems increases, their performance becomes more difficult to predict through modeling and\\u000a simulation. This paper investigates simulation models used to forecast predictions of the performance of engineering systems\\u000a in support of high-consequence decision-making. Specifically this paper directs its attention to the validation of the simulation\\u000a models for certification purposes. Instead of relying on virgin models,

Sezer Atamturktur; François Hemez; Cetin Unal

68

Weather satellites and the economic value of forecasts: evidence from the electric power industry  

Microsoft Academic Search

Data from weather satellites have become integral to the weather forecast process in the United States and abroad. Satellite data are used to derive improved forecasts for short-term routine weather, long-term climate change, and for predicting natural disasters. The resulting forecasts have saved lives, reduced weather-related economic losses, and improved the quality of life. Weather information routinely assists in managing

Henry R. Hertzfeld; Ray A. Williamson; Avery Sen

2004-01-01

69

Near real time wind energy forecasting incorporating wind tunnel modeling  

Microsoft Academic Search

A series of experiments and investigations were carried out to inform the development of a day-ahead wind power forecasting system. An experimental near-real time wind power forecasting system was designed and constructed that operates on a desktop PC and forecasts 12--48 hours in advance. The system uses model output of the Eta regional scale forecast (RSF) to forecast the power

William David Lubitz

2005-01-01

70

Economic analysis on tax model based on BP neural network  

Microsoft Academic Search

It is difficult to accurately analyze forecasting of tax income. This thesis establishes a tax forecasting model based on BP neural network to analyze impacts imposed on tax income by changes of the following economic factors: industrial added value, total investment in fixed assets, total volume of import and export, total volume of fiscal expenditure, resident consumption level, etc. The

Shen Cungen; Zhang Wenzhen

2009-01-01

71

The Hanford Site New Production Reactor (NPR) economic and demographic baseline forecasts  

SciTech Connect

The objective of this is to present baseline employment and population forecasts for Benton, Franklin, and Yakima Counties. These forecasts will be used in the socioeconomic analysis portion of the New Production Reactor Environmental Impact Statement. Aggregate population figures for the three counties in the study area were developed for high- and low-growth scenarios for the study period 1990 through 2040. Age-sex distributions for the three counties during the study period are also presented. The high and low scenarios were developed using high and low employment projections for the Hanford site. Hanford site employment figures were used as input for the HARC-REMI Economic and Demographic (HED) model to produced baseline employment forecasts for the three counties. These results, in turn, provided input to an integrated three-county demographic model. This model, a fairly standard cohort-component model, formalizes the relationship between employment and migration by using migration to equilibrate differences in labor supply and demand. In the resulting population estimates, age-sex distributions for 1981 show the relatively large work force age groups in Benton County while Yakima County reflects higher proportions of the population in the retirement ages. The 2040 forecasts for all three counties reflect the age effects of relatively constant and low fertility increased longevity, as well as the cumulative effects of the migration assumptions in the model. By 2040 the baby boom population will be 75 years and older, contributing to the higher proportion of population in the upper end age group. The low scenario age composition effects are similar. 13 refs., 5 figs., 9 tabs.

Cluett, C.; Clark, D.C. (Battelle Human Affairs Research Center, Seattle, WA (USA)); Pittenger, D.B. (Demographics Lab., Olympia, WA (USA))

1990-08-01

72

Generalized martingale model of the uncertainty evolution of streamflow forecasts  

NASA Astrophysics Data System (ADS)

Streamflow forecasts are dynamically updated in real-time, thus facilitating a process of forecast uncertainty evolution. Forecast uncertainty generally decreases over time and as more hydrologic information becomes available. The process of forecasting and uncertainty updating can be described by the martingale model of forecast evolution (MMFE), which formulates the total forecast uncertainty of a streamflow in one future period as the sum of forecast improvements in the intermediate periods. This study tests the assumptions, i.e., unbiasedness, Gaussianity, temporal independence, and stationarity, of MMFE using real-world streamflow forecast data. The results show that (1) real-world forecasts can be biased and tend to underestimate the actual streamflow, and (2) real-world forecast uncertainty is non-Gaussian and heavy-tailed. Based on these statistical tests, this study proposes a generalized martingale model GMMFE for the simulation of biased and non-Gaussian forecast uncertainties. The new model combines the normal quantile transform (NQT) with MMFE to formulate the uncertainty evolution of real-world streamflow forecasts. Reservoir operations based on a synthetic forecast by GMMFE illustrates that applications of streamflow forecasting facilitate utility improvements and that special attention should be focused on the statistical distribution of forecast uncertainty.

Zhao, Tongtiegang; Zhao, Jianshi; Yang, Dawen; Wang, Hao

2013-07-01

73

Forecast of future aviation fuels: the model. final report  

SciTech Connect

A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

Ayati, M.B.; Liu, C.Y.; English, J.M.

1981-09-01

74

Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose  

NASA Astrophysics Data System (ADS)

The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

Shrestha, D. L.; Robertson, D. E.; Wang, Q. J.; Pagano, T. C.; Hapuarachchi, H. A. P.

2013-05-01

75

Wind Energy Forecasting: The Economic Benefits of Accuracy  

Microsoft Academic Search

Despite the many desirable attributes of wind energy, the fact that wind is an intermittent resource has been a source of concern for utility system operators and managers. State-of-the-art wind power production forecast systems have demonstrated that they can significantly enhance the value of wind generation by increasing system reliability and reducing operating costs. The best forecasts are made with

John Zack; Bruce Bailey; Michael Brower

76

Application of Neural Network-based Combining Forecasting Model Optimized by Ant Colony In Power Load Forecasting  

Microsoft Academic Search

For non-linear and gray of power load forecasting, this paper proposed a new combining forecasting model. First optimize the parameters of the GM(1, 1, ¿) forecasting model with ant colony algorithm, and predict a set of load values; then predict another set of load values with Auto-regressive integrated moving average model (ARIMA). The forecasting results of ant colony gray model

Niu Dongxiao; Wang Hanmei; Cai Chengkai

2010-01-01

77

Forecasting quantitatively using micro\\/meso\\/macro-economics with scenarios for qualitative balance  

Microsoft Academic Search

Purpose – The purpose of the paper is to report on a novel approach to assessing long-term policy and technology impacts. This approach combines a qualitative forecast with a tri-level quantitative projection to provide a broadly socio-economic analysis. It is aimed at forecasting problems, such as impact assessment for future policy formulation in the light of socio-economic, technological and market

Simon Forge

2009-01-01

78

Modeling and Forecasting U.S. Mortality  

Microsoft Academic Search

Time series methods are used to make long-run forecasts, with confidence intervals, of age-specific mortality in the United States from 1990 to 2065. First, the logs of the age-specific death rates are modeled as a linear function of an unobserved period-specific intensity index, with parameters depending on age. This model is fit to the matrix of U.S. death rates, 1933

Ronald D. Lee; Lawrence R. Carter

1992-01-01

79

Efficient testing of earthquake forecasting models  

Microsoft Academic Search

Computationally efficient alternatives are proposed to the likelihood-based tests employed by the Collaboratory for the Study\\u000a of Earthquake Predictability for assessing the performance of earthquake likelihood models in the earthquake forecast testing\\u000a centers. For the conditional L-test, which tests the consistency of the earthquake catalogue with a model, an exact test using convolutions of distributions\\u000a is available when the number

David A. Rhoades; Danijel Schorlemmer; Matthew C. Gerstenberger; Annemarie Christophersen; J. Douglas Zechar; Masajiro Imoto

2011-01-01

80

Neural network models for forecast: a review  

Microsoft Academic Search

Neural networks are advocated as a replacement for statistical forecasting methods. The authors review the literature comparing neural networks and classical forecasting methods, particularly in causal forecasting, time series forecasting, and judgmental forecasting. They provide not only an overview and evaluation of the literature but also summarize several studies performed which address the typical criticisms of work in this area.

Leorey Marquez; Tim Hill; M. O'Connor; W. Remus

1992-01-01

81

Practical overview of ARIMA models for time-series forecasting  

SciTech Connect

Single series analysis methodology is illustrated. The commentary summarizes the Box-Jenkins philosophy and the ARIMA model structure, with particular emphasis on practical aspects of application, forecast interpretation, strengths weaknesses, and comparison to other time series forecasting approaches. (GHT)

Pack, D.J.

1980-01-01

82

Solar activity forecast model supported with artificial intelligence techniques  

NASA Astrophysics Data System (ADS)

Many measures are usually employed in operational solar activity forecast models Different measures provide deferent occurring probability of solar activity A artificial intelligence techniques provides us a best weighting function for deferent measures which determines outputs of a forecast model There are two important tasks in modeling of solar activity forecast One is selection of proper measures and the other is development of new artificial intelligence techniques Solar activity forecast models supported with new artificial intelligence techniques will be developed in National Astronomical Observatories Chinese Academy of Sciences These models will be integrated into one operational forecast system

Wang, H. N.; Han, H.; Du, Z. L.; Cui, Y. M.; Li, R.; Zhang, L. Y.; He, Y. L.

83

Evaluation of numerical weather prediction model precipitation forecasts for use in short-term streamflow forecasting  

NASA Astrophysics Data System (ADS)

The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The high spatial resolution NWP models (ACCESS-A and ACCESS-VT) appear to be relatively free of bias (i.e. <30%) for 24 h total precipitation forecasts. The low resolution models (ACCESS-R and ACCESS-G) have widespread systematic biases as large as 70%. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly) against station observations, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The skill decreases with increasing lead times and the global model ACCESS-G does not have significant skill beyond 7 days. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

Shrestha, D. L.; Robertson, D. E.; Wang, Q. J.; Pagano, T. C.; Hapuarachchi, P.

2012-11-01

84

On the possibility of getting economically sound forecasts of rare space weather events  

NASA Astrophysics Data System (ADS)

There is a problem of setting criteria of space weather forecast quality that allows estimation of the economic effectiveness of forecasts in comparison with other methods for real users. The overwhelming majority of such users (airlines, power lines, pipelines, space exploration, navigation, ground-induced currents, medical services, etc.), are primarily interested in large space weather disturbances that affect the operation of their systems. But powerful disturbances happen rather seldom and so the traditional criteria of quality estimation give very little useful information for an estimate of economic effectiveness of the forecast. This work proposes a specially constructed value “A” for every customer (task) and for each method (or kind) of the forecast, which allows the estimation of the comparative economic effectiveness. Special attention is paid to the statistical significance in reference to the cyclic nature of the solar activity, and there are also indicated some numeral limits, which have to be considered during such a check.

Burov, V. A.

85

A model of the world oil market for project LINK Integrating economics, geology and politics  

Microsoft Academic Search

This paper describes a new model of the world oil market for Project LINK that integrates the effect of changes in the economic, geological and political environment. The model forecasts non-OPEC oil production using a new technique that quantifies the effect of geological, economic and political variables. The model forecasts real oil prices based on changes in market conditions and

Robert K. Kaufmann

1995-01-01

86

Ensemble cloud model applications to forecasting thunderstorms  

NASA Astrophysics Data System (ADS)

A cloud model ensemble forecasting approach is developed to create forecasts which describe the range and distribution of thunderstorm lifetimes that may be expected to occur on a particular day. Such forecasts are crucial for both anticipating severe weather and ensuring the smooth flow of air traffic at busy, hub airports. Storm lifetime is an important characteristic to examine because long-lasting storms tend to produce more significant weather, and have a greater impact on air traffic, than do storms with brief lifetimes. Eighteen days distributed over two warm seasons are examined. Soundings valid at 1800 UTC, 2100 UTC and 0000 UTC, provided by the 0300 UTC run of the operational Mesoeta model from the National Centers for Environmental Prediction, are used to provide initial conditions for the cloud model ensemble. These soundings are from a 160 x 160 km square centered over the location of interest and are shown to represent a likely range of atmospheric states. A minimum threshold value for maximum vertical velocity within the cloud model domain is used to estimate storm lifetime. Forecast storm lifetimes are verified against observed storm lifetimes, as derived from the Storm Cell Identification and Tracking algorithm applied to WSR-88D radar data from the National Weather Service (NWS). When kernel density estimates are applied to the pooled data set consisting of all 18 days, a vertical velocity threshold of 8 m s-1results in a forecast probability density function (pdf) of storm lifetime which is closest to the observed pdf. Model results from all 18 days also reveal that the storm lifetime resulting from a given input sounding cannot be determined by analyzing the bulk sounding parameters, such as convective available potential energy, bulk Richardson number (BRN), BRN shear, or storm relative helicity. Standard 2 x 2 contingency statistics reveal that under certain conditions, the ensemble model displays some skill locating where convection is most likely to occur. Contingency statistics also show that when storm lifetimes of at least 60 min are used as a proxy for severe weather, the ensemble shows considerable skill at identifying days that are likely to produce severe weather. Because the ensemble model appears to have skill in predicting the range and distribution of storm lifetimes on a daily basis, the forecast pdf of storm lifetime is used directly to create probabilistic forecasts of storm lifetime, given the current age of a storm. Such a product could furnish useful information to Air Traffic controllers by providing guidance about how soon a storm is likely to affect (or cease to affect) air traffic at a specific location. Similarly, this product could provide NWS forecasters with guidance about how likely it is that a particular cell will affect a given community.

Elmore, Kimberly Laurence

2000-08-01

87

Model documentation report: Short-term Integrated Forecasting System demand model 1985. [(STIFS)  

SciTech Connect

The Short-Term Integrated Forecasting System (STIFS) Demand Model consists of a set of energy demand and price models that are used to forecast monthly demand and prices of various energy products up to eight quarters in the future. The STIFS demand model is based on monthly data (unless otherwise noted), but the forecast is published on a quarterly basis. All of the forecasts are presented at the national level, and no regional detail is available. The model discussed in this report is the April 1985 version of the STIFS demand model. The relationships described by this model include: the specification of retail energy prices as a function of input prices, seasonal factors, and other significant variables; and the specification of energy demand by product as a function of price, a measure of economic activity, and other appropriate variables. The STIFS demand model is actually a collection of 18 individual models representing the demand for each type of fuel. The individual fuel models are listed below: motor gasoline; nonutility distillate fuel oil, (a) diesel, (b) nondiesel; nonutility residual fuel oil; jet fuel, kerosene-type and naphtha-type; liquefied petroleum gases; petrochemical feedstocks and ethane; kerosene; road oil and asphalt; still gas; petroleum coke; miscellaneous products; coking coal; electric utility coal; retail and general industry coal; electricity generation; nonutility natural gas; and utility petroleum. The demand estimates produced by these models are used in the STIFS integrating model to produce a full energy balance of energy supply, demand, and stock change. These forecasts are published quarterly in the Outlook. Details of the major changes in the forecasting methodology and an evaluation of previous forecast errors are presented once a year in Volume 2 of the Outlook, the Methodology publication.

Not Available

1985-07-01

88

Forecasting next-day electricity prices by time series models  

Microsoft Academic Search

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models.

Francisco J. Nogales; Javier Contreras; Antonio J. Conejo; Rosario Espínola

2002-01-01

89

Battlescale Forecast Model Sensitivity Study.  

National Technical Information Service (NTIS)

Expendable sensors might be used on the battlefield to report weather data not in conformance with normal weather sensor placement and accuracy standards. The purpose of this study was to investigate the impact of errors in surface measurements on a model...

B. Sauter T. Henmi E. Pedrego

2003-01-01

90

Neural Network Models for Time Series Forecasts  

Microsoft Academic Search

Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R.

Tim Hill; Marcus OConnor; William Remus

1996-01-01

91

Periodic ARMA models applied to weekly streamflow forecasts  

Microsoft Academic Search

This paper presents a weekly streamflow forecasting model based on linear ARMA (p, q) models, considering both periodic and nonperiodic models. For each week, fifty possible models are automatically analyzed. The best modeling and parameter estimation are chosen based on the minimum square mean forecast error of the whole time series. The proposed model, which has been validated by the

M. E. P. Maceira; J. M. Damazio; A. O. Ghirardi; H. M. Dantas

1999-01-01

92

Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates  

Microsoft Academic Search

It has been documented that random walk outperforms most economic structural and time series models in out-of-sample forecasts of the conditional mean dynamics of exchange rates. In this paper, we study whether random walk has similar dominance in out-of-sample forecasts of the conditional probability density of exchange rates given that the probability density forecasts are often needed in many applications

Yongmiao Hong; Haitao Li; Feng Zhao

2007-01-01

93

The NASA Forecast Model Web Map Service  

NASA Astrophysics Data System (ADS)

The NASA Forecast Model WMS (NFMW) provides on-demand visualizations of Earth science data. The current usage focuses on field campaigns and other projects that use the output of the Goddard Earth Observing System (GEOS) and Weather Research Framework (WRF) models, but other models can be supported. The NFMW implements the Open Geospatial Consortium (OGC) Web Map Service (WMS) interoperability specification. WMS provisions for handling time and other dimensions are used extensively to support the multi-dimensional nature of the forecast data. Scientists and other interested parties access the WMS using a variety of clients including our own web-based Viewer, Google Earth, a multi-screen Hyperwall installation, or other WMS-compliant applications. We have found that offering an open-standard interface to our data collection has simplified usage of the data and has permitted users to visualize the data from remote locations using only a web browser. The NFMW and Viewer may be accessed at http://map.nasa.gov/tools.html. The NFMW software is available as open source, and is written in a combination of Perl and Interactive Data Language (IDL). This work is supported by the Geosciences Interoperability Office (GIO) and the Modeling and Analysis Program (MAP) at NASA.

de La Beaujardière, J.

2007-12-01

94

Improving streamflow forecasts using a dynamic model combination approach  

Microsoft Academic Search

This study presents a new rationale for medium to long-term forecasting of streamflow at multiple locations in a catchment using a dynamic model combination approach. The dynamic model combination is presented as a pair-wise combination of multiple forecasting models using a hierarchical structure than aims to pair the models on the basis of the lowest commonality they contain. Unlike the

A. Sharma; S. Chowdhury

2008-01-01

95

Modeling of energy economics  

Microsoft Academic Search

Descriptions, with flow charts and diagrams, outline three models used in energy economics. The Electric Utility Model describes finances of a regulated utility based on demand projections, debt interest rates, preferred dividend rates, and the expected rate of return on equity. Decision rules on using long-term debts, preferred, or common stock to raise capital are incorporated in such a way

M. S. Carliner; I. Gershkoff

1975-01-01

96

A Cost Performance Forecasting Concept and Model.  

National Technical Information Service (NTIS)

This report identifies and illustrates the principles of a new and potentially valuable cost forecasting method. It is the objective of the technique to forecast Estimates At Completion (EACs) each month, utilizing data available in the Cost Performance R...

O. A. Karsch

1974-01-01

97

Wind power forecasting by an empirical model using NWP outputs  

Microsoft Academic Search

This paper presents a simple and robust wind power forecasting approach using inputs from a state-of-the-art numerical weather prediction models (NWP) with mesoscale resolution. The model can be used for short-term and longer term forecasting horizon up to 72 hours ahead. The forecasting ability of the presented approach is demonstrated using real power production data from the Czech Republic.

E. Pelikan; K. Eben; J. Resler; P. Jurus; P. Krc; M. Brabec; T. Brabec; P. Musilek

2010-01-01

98

A wavelet neural network conjunction model for groundwater level forecasting  

NASA Astrophysics Data System (ADS)

A new method of wavelet-neural network groundwater level forecasting is proposed. New method compared to neural network and ARIMA models. Variables:monthly total precipitation; average temperature; average groundwater level. Data: 2002-2009 at two sites in the Chateauguay watershed in Quebec, Canada. WA-ANN models provided more accurate forecasts compared to ANN and ARIMA models.

Adamowski, Jan; Chan, Hiu Fung

2011-09-01

99

Improving forecasting for telemarketing centers by ARIMA modeling with intervention  

Microsoft Academic Search

In this study we analyze existing and improved methods for forecasting incoming calls to telemarketing centers for the purposes of planning and budgeting. We analyze the use of additive and multiplicative versions of Holt–Winters (HW) exponentially weighted moving average models and compare it to Box–Jenkins (ARIMA) modeling with intervention analysis. We determine the forecasting accuracy of HW and ARIMA models

Lisa Bianchi; Jeffrey Jarrett; R. Choudary Hanumara

1998-01-01

100

Downscaled seasonal forecasts using an ensemble of regional models  

NASA Astrophysics Data System (ADS)

The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional effort to evaluate the usefulness of dynamically downscaled global seasonal forecasts. Seven regional climate models have downscaled 10-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) for each winter season (December-April) of 1982-2003. The target region for downscaling is the continental United States. MRED investigators also have developed methods and metrics for analysis of downscaled forecasts. These include an Added Value Index that quantifies skill improvement of a downscaled forecast compared to the corresponding global forecast. Results show that added value from downscaling depends on location, forecast variable, and lead time. Locations with added value are generally in the western United States, and added value tends to be greater for precipitation than for temperature. Downscaled forecasts have greatest skill for seasonal precipitation anomalies in strong El Niño events such as 1982-83 and 1997-98. In most circumstances area averaged seasonal precipitation for the regional models closely tracks the corresponding results for the global model, though with an offset that varies considerably amongst the regional models. There is large spread amongst the 15 CFS ensemble members and this carries through to the corresponding downscaled forecasts. Because of the strong dependence of downscaled results on the global model, future experiments should test the use of multiple global models downscaled by multiple regional models.

Arritt, R.

2012-04-01

101

On-line economic optimization of energy systems using weather forecast information.  

SciTech Connect

We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.

Zavala, V. M.; Constantinescu, E. M.; Krause, T.; Anitescu, M.

2009-01-01

102

On the clustering of climate models in ensemble seasonal forecasting  

NASA Astrophysics Data System (ADS)

Multi-model ensemble seasonal forecasting system has expanded in recent years, with a dozen coupled climate models around the world being used to produce hindcasts or real-time forecasts. However, many models are sharing similar atmospheric or oceanic components which may result in similar forecasts. This raises questions of whether the ensemble is over-confident if we treat each model equally, or whether we can obtain an effective subset of models that can retain predictability and skill as well. In this study, we use a hierarchical clustering method based on inverse trigonometric cosine function of the anomaly correlation of pairwise model hindcasts to measure the similarities among twelve American and European seasonal forecast models. Though similarities are found between models sharing the same atmospheric component, different versions of models from the same center sometimes produce quite different temperature forecasts, which indicate that detailed physics packages such as radiation and land surface schemes need to be analyzed in interpreting the clustering result. Uncertainties in clustering for different forecast lead times also make reducing redundant models more complicated. Predictability analysis shows that multi-model ensemble is not necessarily better than a single model, while the cluster ensemble shows consistent improvement against individual models. The eight model-based cluster ensemble forecast shows comparable performance to the total twelve model ensemble in terms of probabilistic forecast skill for accuracy and discrimination. This study also manifests that models developed in U.S. and Europe are more independent from each other, suggesting the necessity of international collaboration in enhancing multi-model ensemble seasonal forecasting.

Yuan, Xing; Wood, Eric F.

2012-09-01

103

Forecasts covering one month using a cut-cell model  

NASA Astrophysics Data System (ADS)

This paper investigates the impact and potential use of the cut-cell vertical discretisation for forecasts covering five days and climate simulations. A first indication of the usefulness of this new method is obtained by a set of five-day forecasts, covering January 1989 with six forecasts. The model area was chosen to include much of Asia, the Himalayas and Australia. The cut-cell model LMZ (Lokal Modell with z-coordinates) provides a much more accurate representation of mountains on model forecasts than the terrain-following coordinate used for comparison. Therefore we are in particular interested in potential forecast improvements in the target area downwind of the Himalayas, over southeastern China, Korea and Japan. The LMZ has previously been tested extensively for one-day forecasts on a European area. Following indications of a reduced temperature error for the short forecasts, this paper investigates the model error for five days in an area influenced by strong orography. The forecasts indicated a strong impact of the cut-cell discretisation on forecast quality. The cut-cell model is available only for an older (2003) version of the model LM (Lokal Modell). It was compared using a control model differing by the use of the terrain-following coordinate only. The cut-cell model improved the precipitation forecasts of this old control model everywhere by a large margin. An improved, more transferable version of the terrain-following model LM has been developed since then under the name CLM (Climate version of the Lokal Modell). The CLM has been used and tested in all climates, while the LM was used for small areas in higher latitudes. The precipitation forecasts of the cut-cell model were compared also to the CLM. As the cut-cell model LMZ did not incorporate the developments for CLM since 2003, the precipitation forecast of the CLM was not improved in all aspects. However, for the target area downstream of the Himalayas, the cut-cell model considerably improved the prediction of the monthly precipitation forecast even in comparison with the modern CLM version. The cut-cell discretisation seems to improve in particular the localisation of precipitation, while the improvements leading from LM to CLM had a positive effect mainly on amplitude.

Steppeler, J.; Park, S.-H.; Dobler, A.

2013-07-01

104

Evaluating the model forecasts of plume evolution in BORTAS  

NASA Astrophysics Data System (ADS)

We develop a novel forecast quality measure that is designed to reflect the 3-dimensional structure of biomass burning plumes and use it to evaluate the quality of the plume forecasts that were produced during the "Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites" (BORTAS) project. In recent years, several approaches have been developed to quantify numerical forecast errors, but most are designed for 2-dimensional fields and do not consider the 3-dimensional structure of plumes, in particular the altitude of individual plume features. Here, we extend a displacement-based method to include the vertical dimension. The algorithm calculates the 3-dimensional displacement that would be needed to transform the forecast field into the observed field. This displacement then forms the basis for a quantitative forecast quality measure. This method is used to evaluate model forecasts of boreal wildfire plumes within the BORTAS project. During the BORTAS intense measurement campaign (Summer 2012), forecasts of carbon monoxide concentrations from boreal biomass burning over North America and the North Atlantic were produced twice daily over several weeks using the NASA GEOS-5 model for operational purposes. We analyse the quality of the forecasts and discuss areas and meteorological situations that influence the local forecast quality.

Matthiesen, Stephan; Palmer, Paul I.; Parrington, Mark

2013-04-01

105

Sequential forecast of incident duration using Artificial Neural Network models.  

PubMed

This study creates an adaptive procedure for sequential forecasting of incident duration. This adaptive procedure includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration. Model A is used to forecast the duration time at the time of incident notification, while Model B provides multi-period updates of duration time after the incident notification. These two models together provide a sequential forecast of incident duration from the point of incident notification to the incident road clearance. Model inputs include incident characteristics, traffic data, time gap, space gap, and geometric characteristics. The model performance of mean absolute percentage error for forecasted incident duration at each time point of forecast are mostly under 40%, which indicates that the proposed models have a reasonable forecast ability. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an incident is reported. Thereby travelers and traffic management units can better understand the impact of the existing incident. Based on the model effect assessments, this study shows that the proposed models are feasible in the Intelligent Transportation Systems (ITS) context. PMID:17303059

Wei, Chien-Hung; Lee, Ying

2007-02-14

106

NEURAL NETWORK MODELS FOR ELECTRICITY MARKET FORECASTING  

Microsoft Academic Search

1 ABSTRACT For increasingly deregulated electricity market, accurate forecasting of electricity demand and spot price has become crucial for the independent system operators, generators and consumers. However, the exclusive features of electricity present a number of challenges for this task. This paper describes the author's continual effort in power system forecasting studies. By using various new techniques, such as wavelet,

Z. Xu; Z. Y. Dong; W. Q. Liu

107

A channel dynamics model for real-time flood forecasting  

NASA Astrophysics Data System (ADS)

A new channel dynamics scheme (alternative system predictor in real time (ASPIRE)), designed specifically for real-time river flow forecasting, is introduced to reduce uncertainty in the forecast. ASPIRE is a storage routing model that limits the influence of catchment model forecast errors to the downstream station closest to the catchment. Comparisons with the Muskingum routing scheme in field tests suggest that the ASPIRE scheme can provide more accurate forecasts, probably because discharge observations are used to a maximum advantage and routing reaches (and model errors in each reach) are uncoupled. Using ASPIRE in conjunction with the Kalman filter did not improve forecast accuracy relative to a deterministic updating procedure. Theoretical analysis suggests that this is due to a large process noise to measurement noise ratio.

Hoos, Anne B.; Koussis, Antonis D.; Beale, Guy O.

1989-04-01

108

Monthly Mean Forecast Experiments with the GISS Model.  

National Technical Information Service (NTIS)

The GISS general circulation model was used to compute global monthly mean forecasts for January 1973, 1974, and 1975 from initial conditions on the first day of each month and constant sea surface temperatures. Forecasts were evaluated in terms of global...

E. Kuo J. Spar R. Atlas

1976-01-01

109

Forecasting Conflict in the Balkans using Hidden Markov Models  

Microsoft Academic Search

This study uses hidden Markov models (HMM) to forecast conflict in the former Yugoslavia for the period January 1991 through January 1999. The political and military events reported in the lead sentences of Reuters news service stories were coded into the World Events Interaction Survey (WEIS) event data scheme. The forecasting scheme involved randomly selecting eight 100-event “templates” taken at

Philip A. Schrodt

110

Cross-Validation in Statistical Climate Forecast Models  

Microsoft Academic Search

Cross-validation is a statistical procedure that produces an estimate of forecast skill which is less biased than the usual hindcast skill estimates. The cross-validation method systematically deletes one or more cases in a dataset, derives a forecast model from the remaining cases, and tests it on the deleted case or cases. The procedure is nonparametric and can be applied to

Joel Michaelsen

1987-01-01

111

Operational forecasting based on a modified Weather Research and Forecasting model  

SciTech Connect

Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18

112

Rainfall stochastic modeling for runoff forecasting  

NASA Astrophysics Data System (ADS)

Rainfall fields estimation over a catchment area is an important stage in many hydrological applications. In this context, weather radars have several advantages because a single-site can scan a vast area with very high temporal and spatial resolution. The construction of weather radar systems with dual polarization capability allowed progress on radar rainfall estimation and its hydro-meteorological applications. For these applications of radar data it is necessary to remove the ground clutter contamination with an algorithm based on the backscattering signal variance of the differential reflectivity. The calibration of the GDSTM model (Gaussian Displacements Spatial Temporal Model), a cluster stochastic generation model in continuous space and time, is herewith presented. In this model, storms arrive in a Poisson process in time with cells occurring in each storm that cluster in space and time. The model is calibrated, using data collected by the weather radar Polar 55C located in Rome, inside a square area of 132 × 132 km2, with the radar at the centre. The GDSTM is fitted to sequences of radar images with a time interval between the PPIs scans of 5 min. A generalized method of moment procedure is used for parameter estimation. For the validation of the ability of the model to reproduce internal structure of rain event, a geo-morphological rainfall-runoff model, based on width function (WFIUH), was calibrated using simulated and observed data. Several rainfall fields are generated with the stochastic model and later they are used as input of the WFIUH model so that the forecast discharges can be compared to the observed ones.

Russo, Fabio; Lombardo, Federico; Napolitano, Francesco; Gorgucci, Eugenio

113

Visibility Parameterization For Forecasting Model Applications  

NASA Astrophysics Data System (ADS)

In this study, the visibility parameterizations developed during Fog Remote Sensing And Modeling (FRAM) projects, conducted in central and eastern Canada, will be summarized and their use for forecasting/nowcasting applications will be discussed. Parameterizations developed for reductions in visibility due to 1) fog, 2) rain, 3) snow, and 4) relative humidity (RH) during FRAM will be given and uncertainties in the parameterizations will be discussed. Comparisons made between Canadian GEM NWP model (with 1 and 2.5 km horizontal grid spacing) and observations collected during the Science of Nowcasting Winter Weather for Vancouver 2010 (SNOW-V10) project and FRAM projects, using the new parameterizations, will be given Observations used in this study were obtained using a fog measuring device (FMD) for fog parameterization, a Vaisala all weather precipitation sensor called FD12P for rain and snow parameterizations and visibility measurements, and a total precipitation sensor (TPS), and distrometers called OTT ParSiVel and Laser Precipitation Measurement (LPM) for rain/snow particle spectra. The results from the three SNOW-V10 sites suggested that visibility values given by the GEM model using the new parameterizations were comparable with observed visibility values when model based input parameters such as liquid water content, RH, and precipitation rate for visibility parameterizations were predicted accurately.

Gultepe, I.; Milbrandt, J.; Binbin, Z.

2010-07-01

114

Weighted fuzzy time series models for TAIEX forecasting  

NASA Astrophysics Data System (ADS)

This study proposes weighted models to tackle two issues in fuzzy time series forecasting, namely, recurrence and weighting. It is argued that recurrent fuzzy relationships, which were simply ignored in previous studies, should be considered in forecasting. It is also recommended that different weights be assigned to various fuzzy relationships. In previous studies, these fuzzy relationships were treated as if they were equally important, which might not have properly reflected the importance of each individual fuzzy relationship in forecasting. The weighted models are compared with the local regression models in which weight functions also play an important role. Both models are different by nature, but certain theoretical backgrounds in local regression models are adopted. By using the Taiwan stock index as the forecasting target, the empirical results show that the weighted model outperforms one of the conventional fuzzy time series models.

Yu, Hui-Kuang

2005-04-01

115

Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction  

NASA Astrophysics Data System (ADS)

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.

Maycock, Amanda C.; Keeley, Sarah P. E.; Charlton-Perez, Andrew J.; Doblas-Reyes, Francisco J.

2011-01-01

116

Evaluation of Experimental Models for Tropical Cyclone Forecasting in Support of the NOAA Hurricane Forecast Improvement Project (HFIP)  

NASA Astrophysics Data System (ADS)

The Tropical Cyclone Modeling Team (TCMT) in NCAR's Joint Numerical Testbed (JNT) Program focuses on the verification of experimental forecasts of tropical cyclones (TCs). Activities of the team include the development of new verification methods and tools for TC forecasts and the design and implementation of diagnostic verification experiments to evaluate the performance of tropical cyclone forecast models. For the Hurricane Forecast Improvement Project (HFIP), the TCMT has designed and conducted verification studies involving various regional and global forecast models that participate in the annual HFIP retrospective and real-time forecast demonstration studies. The TCMT has also developed new statistical approaches that provide statistically meaningful diagnostic evaluations of TC forecasts. These methods include new diagnostic tools to aid, for example, in the evaluation of track and intensity errors and ensemble forecasts. Recently, the TCMT conducted a retrospective evaluation of eight experimental tropical cyclone forecast models that ranged from deterministic to ensemble forecast systems. These models were evaluated for storms that occurred in the 2008-2010 hurricane seasons in the North Atlantic and Eastern Pacific Oceans. The forecasts from these models were also evaluated for the 2011 HFIP demonstration experiment. This presentation will provide an overview of the evaluation methodology including new methods along with a summary of key results from the 2011 HFIP retrospective and demonstration studies.

Kucera, P. A.; Brown, B. G.; Nance, L.; Williams, C.

2012-04-01

117

Integrated Ecological-Economic Models  

Microsoft Academic Search

Scientific evidence suggests that economic activity is threatening global biodiversity in ways that could severely degrade nature's flow of ecosystem services. Yet, there is relatively little work in economics that addresses biodiversity loss. Some economists have called for better integration of economic and ecological models to address biodiversity and the attendant ecosystem services. Current integrated approaches in economics are discussed,

John Tschirhart

2009-01-01

118

A Modeler's Perspective on Space Weather Forecasting (Invited)  

NASA Astrophysics Data System (ADS)

Space physics is moving into a new era where numerical models originally developed for answering science questions are used as the basis for making operational space weather forecasts. Answering this challenge requires developments on multiple fronts requiring collaborations across space physics disciplines and between the research and operations communities. Since space weather in geospace is driven by the solar wind conditions a natural solution to improving the forecast lead time is to couple geospace models to heliospheric models. The quality of these forecast is dependent upon the ability of the heliospheric models to accurately model IMF Bz. Another challenge presented by moving into the forecasting arena is preparing the models for real-time operation which includes both computational performance and data redundancy issues. Moving into operations also presents modelers with an opportunity to assess their models performance over calculation intervals unprecedented duration. A key collaboration in the transition of models to operation is the discussion between forecasters and developers on what forecast parameters can accurately be predicted by the current generation of numerical models. This collaboration naturally includes a discussion of the definition of the best metrics to be used in quantitatively assessing performance.

Wiltberger, M. J.

2010-12-01

119

Multivariable grey dynamic forecasting model based on complex network  

Microsoft Academic Search

Multi-variable grey dynamic forecasting model is a main model of grey systems theory. In this paper, we constructed the discrete grey model of multi-variables. We contrasted the model with GM (n, h) model and the result showed two models are equal to each other through data transformation. Then we could build the bridge of discrete grey model and traditional grey

Xie Nai-ming; Yao Tian-xiang; Liu Si-feng

2009-01-01

120

Point-Specific Wind Forecasting using the HARMONIE Mesoscale Forecast Model with Bayes Model Averaging for Fine-Tuning  

NASA Astrophysics Data System (ADS)

Two distinct elements seem to be required to make accurate wind-speed forecasts for wind-farms: the first is deterministic output from a weather forecast model, and the second is some probabilistic or statistical post-processing to account for local biases, or systematic errors in the model. A variety of statistical post-processing schemes are available, and are generally worthwhile since they are cheap and at worst do no harm. More typically, they demonstrably improve the accuracy of the deterministic forecast. Gridded output from the operational HARMONIE mesoscale weather forecast model has been interpolated to forecast winds at the precise (3-dimensional) location of the met-mast of a wind farm in southwest Ireland. A sequence of 48-hour forecasts run at 6-hourly intervals for over one year have been validated against winds recorded at turbine height on the mast. All the interpolated deterministic forecasts are also post-processed using Bayesian Model Averaging (BMA) to remove systematic local bias, and to provide forecasts in a calibrated probabilistic format. Three variants of the HARMONIE model were also run during October 2010 and validated against the winds recorded at the met-mast. The HARMONIE variant with the most advanced physics and the larger domain was the most accurate in forecasting met-mast windspeed, with mean average error (MAE) of 1.5 ms-1 (i.e., about 10% of mean wind speed). The BMA analysis for this short period (using a 25-day training period) did not change the MAE for the best HARMONIE configuration, but did improve the MAE of the other two by about 15%. The most advanced HARMONIE configuration proved more accurate than an ensemble of all three. There was negligible degradation in the skill of the hourly forecasts, at least out to 24 hours (i.e., 24-hr forecasts were only marginally less accurate than 0-hr analyses or 1-hr forecasts). Results are presented from the operational 48-hr HARMONIE forecasts collected during Jan.-Mar. 2012, as compared with recorded winds at the met-mast. The added value of BMA post-processing (using a moving 25-day training period) is quantified. Forecasts from an experimental extra high-resolution HARMONIE (1km resolution, on a 1,000 x 1,000 km domain) are available for a continuous 30-day period starting 10 Nov. 2012, and the extra skill provided by this for the specific wind-farm site is also quantified.

Peters, Martin; McKinstry, Alastair; O'Brien, Enda; Ralph, Adam; Sheehy, Michael

2013-04-01

121

A refined fuzzy time series model for stock market forecasting  

NASA Astrophysics Data System (ADS)

Time series models have been used to make predictions of stock prices, academic enrollments, weather, road accident casualties, etc. In this paper we present a simple time-variant fuzzy time series forecasting method. The proposed method uses heuristic approach to define frequency-density-based partitions of the universe of discourse. We have proposed a fuzzy metric to use the frequency-density-based partitioning. The proposed fuzzy metric also uses a trend predictor to calculate the forecast. The new method is applied for forecasting TAIEX and enrollments’ forecasting of the University of Alabama. It is shown that the proposed method work with higher accuracy as compared to other fuzzy time series methods developed for forecasting TAIEX and enrollments of the University of Alabama.

Jilani, Tahseen Ahmed; Burney, Syed Muhammad Aqil

2008-05-01

122

Survey Design for a Statewide Multimodal Transportation Forecasting Model.  

National Technical Information Service (NTIS)

In 1990, the NMSHTD initiated an ambitious and long-term research project. The project was to define the process for and undertake the development of a statewide multimodal transportation forecasting model. The project commenced in April, 1991. The first ...

D. Kurth R. Donnelly B. Arens J. Hamburg W. Davidson

1992-01-01

123

A Statistical Model for Forecasting Podiatric Manpower Requirements.  

National Technical Information Service (NTIS)

The report includes an evaluation of current data on podiatry manpower, description of podiatry forecasting models developed, and recommendations for future podiatry manpower studies. The report is presented in four volumes: Volume I. Report Narrative and...

S. P. Nyman L. G. Buttell

1974-01-01

124

Road Surface Crack Condition Forecasting Using Neural Network Models.  

National Technical Information Service (NTIS)

This report summarizes the results obtained from a research project sponsored by Florida Department of Transportation to develop a Backpropagation Neural Network (BPNN) model for the forecasting of pavement crack condition of Florida's highway network. Th...

Z. Lou J. J. Lu M. Gunaratne

1999-01-01

125

Forecasting of Annual Streamflow Using Data-Driven Modeling Approach  

Microsoft Academic Search

In a water-stressed region, such as the western United States, it is essential to have long lead-time streamflow forecast for reservoir operation and water resources management. In this study, we develop and examine the accuracy of a data driven model incorporating large-scale climate information for extending streamflow forecast lead-time. A data driven model i.e. Support Vector Machine (SVM) based on

A. Kalra; W. P. Miller; S. Ahmad; K. W. Lamb

2010-01-01

126

Weather satellites and the economic value of forecasts: evidence from the electric power industry  

NASA Astrophysics Data System (ADS)

Data from weather satellites have become integral to the weather forecast process in the United States and abroad. Satellite data are used to derive improved forecasts for short-term routine weather, long-term climate change, and for predicting natural disasters. The resulting forecasts have saved lives, reduced weather-related economic losses, and improved the quality of life. Weather information routinely assists in managing resources more efficiently and reducing industrial operating costs. The electric energy industry in particular makes extensive use of weather information supplied by both government and commercial suppliers. Through direct purchases of weather data and information, and through participating in the increasing market for weather derivatives, this sector provides measurable indicators of the economic importance of weather information. Space weather in the form of magnetic disturbances caused by coronal mass ejections from the sun creates geomagnetically induced currents that disturb the electric power grid, sometimes causing significant economic impacts on electric power distribution. This paper examines the use of space-derived weather information on the U.S. electric power industry. It also explores issues that may impair the most optimum use of the information and reviews the longer-term opportunities for employing weather data acquired from satellites in future commercial and government activity.

Hertzfeld, Henry R.; Williamson, Ray A.; Sen, Avery

2004-08-01

127

Combining regional forecast and crop yield models for the USDA  

NASA Astrophysics Data System (ADS)

Besides the risk of different economic and market conditions, large agricultural interests face the risk of crop losses from a number of weather-related perils including drought and heat, excess moisture, hail, frost and freeze, and wind. In a joint project, AIR Worldwide and Agrilogic are teamed with the RMA(Risk Management Agency) component of the USDA (United States Department of Agriculture) in developing InsuranceVision, a tool to support the producer in crop insurance decision-making. The tool will use available climatic, agronomic and econometric models to analyze likely scenarios over the growing season and project probable yields and prices by harvest. The tool will ultimately assist growers in deciding what insurance products will best minimize their market risk. This presentation focuses on the weather/climate related models based on the NCAR-NCEP Global Reanalysis Project data set, the NCAR Community Climate Model (CCM 3.6) and the 5th generation NCAR-Penn State University Mesoscale Model (MM5). A method will be discussed that derives crop yield probability distributions from historical detrended yield data, numerical weather model climatologies, climate projections and locally refined forecasts.

Zuba, G.; Gibbas, M.; Lee, M.; Dailey, P.; Keller, J.

2003-04-01

128

Spatio-temporal modeling for real-time ozone forecasting  

PubMed Central

The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.

Paci, Lucia; Gelfand, Alan E.; Holland, David M.

2013-01-01

129

Post-Processing for the Battlescale Forecast Model and Mesoscale Model Version 5.  

National Technical Information Service (NTIS)

The Battlescale Forecast Model (BFM) produces many forecasting parameters including temperature, pressure, dewpoint, relative humidity, wind information, as well as precipitation amounts. While these output data provide valuable weather information Tactic...

J. E. Passner

2003-01-01

130

A Modeling Framework for Improved Agricultural Water Supply Forecasting  

NASA Astrophysics Data System (ADS)

The National Water and Climate Center (NWCC) of the USDA Natural Resources Conservation Service is moving to augment seasonal, regression-equation based water supply forecasts with distributed-parameter, physical process models enabling daily, weekly, and seasonal forecasting using an Ensemble Streamflow Prediction (ESP) methodology. This effort involves the development and implementation of a modeling framework, and associated models and tools, to provide timely forecasts for use by the agricultural community in the western United States where snowmelt is a major source of water supply. The framework selected to support this integration is the USDA Object Modeling System (OMS). OMS is a Java-based modular modeling framework for model development, testing, and deployment. It consists of a library of stand-alone science, control, and database components (modules), and a means to assemble selected components into a modeling package that is customized to the problem, data constraints, and scale of application. The framework is supported by utility modules that provide a variety of data management, land unit delineation and parameterization, sensitivity analysis, calibration, statistical analysis, and visualization capabilities. OMS uses an open source software approach to enable all members of the scientific community to collaboratively work on addressing the many complex issues associated with the design, development, and application of distributed hydrological and environmental models. A long-term goal in the development of these water-supply forecasting capabilities is the implementation of an ensemble modeling approach. This would provide forecasts using the results of multiple hydrologic models run on each basin.

Leavesley, G. H.; David, O.; Garen, D. C.; Lea, J.; Marron, J. K.; Pagano, T. C.; Perkins, T. R.; Strobel, M. L.

2008-12-01

131

Using model derived regional climate forecasts to enhance the effectiveness and skill of selected application models in reducing negative impacts  

NASA Astrophysics Data System (ADS)

A new approach to regional climate forecasting in Southern Africa is involving a cross section of researchers working to integrate the key elements of the global system that determine seasonal conditions. The aim is to produce seasonal forecasts of temperature and rainfall with a 1-3 month lead-time. These forecasts, reflecting climatic variation and inter-annual change, using a combination of global and regional climate models, can be used as input for a selection of crop-yield/ hydrological/ economic models to assess the impact and usefulness in specific application areas e.g. water resources, agriculture etc. The investigation focuses on the usefulness of the information content of the forecast output. The impacts of severe droughts and flooding associated with ENSO events can be prepared for and reduced. However, until recently (Vogel, 2000; Mukara, 2000) the value of these forecasts for farming, industry and commerce in South Africa has not been assessed. An essential part of the analysis is the collaboration with others working within the forecaster-user dynamic. This ensures that forecast/model output provides the most usable content for end-users whether in small scale pastoral or commercial farming, hydrological planning, industry or fishing. Input from the users informs the modellers with respect to the format and content of forecast outputs. The parameters most useful to user applications are identified and in consultation with the modellers, specified in the model output. Different model runs are compared and various hindcasts performed. The issue is to determine the level and scope of the accuracy of the identified parameters. A model's accuracy may be temporally substantial, but spatially unreliable. When submitting the seasonal forecast data into other models within a localised region, specific accuracy for that region, during the particular season and in the individual topography, is essential. If the accuracy is lower than a critical value, then the forecast must be deemed no more useful than persistence. The usefulness of model output in existing application models must be maximised. Various models have been used to identify the impacts of climatic change on agriculture (e.g. ACRU and CERES), hydrology (e.g. ACRU) and global warming (e.g. MAGICC). Many of these models have used GCM data and it remains to be investigated how the regional model data can best be beneficial in this regard.

Johnston, P. A.; Hewitson, B. C.

2001-05-01

132

Distributed Hydrologic Models for Flow Forecasts - Part 2  

NSDL National Science Digital Library

Distributed Hydrologic Models for Flow Forecasts Part 2 is the second release in a two-part series focused on the science of distributed models and their applicability to different flow forecasting situations. Presented by Dr. Dennis Johnson, the module provides a more detailed look at the processes and mechanisms involved in distributed hydrologic models. It examines the rainfall/runoff component, snowmelt, overland flow routing, and channel response in a basin as represented in a distributed model. Calibration issues and situations in which distributed hydrologic models might be most appropriate are also addressed.

2011-01-01

133

Vessel Traffic Flow Forecasting Model Study Based on Support Vector Machine  

NASA Astrophysics Data System (ADS)

Based on vessel traffic flow data and Support Vector Machine theory, SVM regression model for short-term vessel traffic flow forecasting was presented. The forecasted vessel traffic flow and abserved ones, which by SVM regression model, coincide properly, and the forecasting results show that mean absolute percentage error of forecasting are smaller than that by SPSS regress model, which validates the feasibility of SVM regression model in the vessel traffic flow forecasting.

Feng, Hongxiang; Kong, Fancun; Xiao, Yingjie

134

The quest for physically realistic streamflow forecasting models  

NASA Astrophysics Data System (ADS)

The current generation of time stepping hydrological models used by operational forecasting agencies are process-weak, where model parameters are often assigned unrealistic values to compensate for model structural weaknesses. These time stepping simulation models are therefore subject to the same stationarity predicament that plagues statistical streamflow forecasting systems. Consequently, the operational forecasting community has similar research priorities to the science community, that is, to develop physically realistic hydrological models. This paper describes development of a new modeling framework to improve the representation of hydrological processes within operational streamflow forecasting models. The framework recognizes that the majority of process-based models use the same set of physics - most models use Darcy's Law to represent the flow of water through the soil matrix and Fourier's Law for thermodynamics. The new modeling framework uses numerically robust solutions of the hydrology and thermodynamic governing equations as the structural core, and incorporates multiple options to represent the impact of different modeling decisions, including different methods to represent spatial variability and different parameterizations of surface fluxes and shallow groundwater. Use of multivariate research data to evaluate these different modeling options reveals that the new modeling framework can provide realistic simulations of both point-scale measurements of hydrologic states and fluxes as well as realistic simulations of streamflow in headwater catchments, with minimal calibration. Moreover, the availability of multiple modeling options improves representation of model uncertainty.

Restrepo, Pedro; Wood, Andy; Clark, Martyn

2013-04-01

135

Industrial end-use planning methodology (INDEPTH): Demonstration of design: Volume 1, Econometric forecasting models: Interim report  

SciTech Connect

The INDEPTH industrial planning methodology will enable utilities to forecast service area electricity demand. The system allows the user to develop energy forecasts for the whole industrial sector, to examine industries most important to the service area, and to study uses of electricity that are of interest in demand-side management programs. The econometric model in this volume forecasts energy use for the entire industrial sector using a set of simultaneous factor demand equations with an imposed structure derived from the economic theory of cost-minimizing behavior.

Andrews, L.M.; King, M.J.; Leary, N.; Perry, D.M.; Snow, C.C.

1986-12-01

136

The skill of precipitation and surface temperature forecasts by the NMC global model during DERF II. [NMC (National Meteorological Center) DERF II (Dynamical Extended Range Forecast II)  

SciTech Connect

This study assesses the skill of forecasts of precipitation and surface temperature by the National Meteorological Center's (NMC) global model in the 108 consecutive 30-day forecasts (known as Dynamical Extended Range Forecast II (DERF II)) that were made from initial conditions 24 h apart between 14 December 1986 and 31 March 1987. Model precipitation accumulated during the first 24 h of each 30-day forecast was used for verification. Anomalies were calculated by averaging the precipitation for a given forecast length over all 108 forecasts and subtracting the resulting mean from the precipitation for that forecast length. A similar procedure was used for surface skin temperature. The skill of the model's forecasts of precipitation and surface temperature anomalies was assessed, using anomalies from day-1 forecasts as verification. Precipitation forecasts for all regions of the globe exhibit more skill than persistence. Precipitation forecasts for the Northern Hemisphere (NH) extratropics show skill 1.5 days further into the forecasts than forecasts for the tropics and exceed the mean skill of 1-day persistence forecasts until day 7. Even the worst individual forecast for the NH extratropics exceeds the mean skill of persistence through day 5. Time-mean precipitation forecasts for the NH extratropics display an anomaly correlation of 0.69 for forecast days 2-5 and 0.53 for forecast days 2-10 when verified against day-1 precipitation anomalies. Surface skin-temperature anomalies are more persistent than precipitation anomalies; forecasts of surface temperature anomalies have higher skill than forecasts of precipitation anomalies. Forecasts of time-mean surface temperature anomalies for the NH extratropics for forecast days 2-30 and 11-30 exhibit levels of skill similar to forecasts of time-mean precipitation anomalies. This implies that forecast skill for such long forecast periods reflects skill in predicting planetary-scale variations. 26 refs., 7 figs., 1 tab.

White, G.H.; Kalnay, E.; Gardner, R.; Kanamitsu, M. (National Meteorological Center, Washington, DC (United States))

1993-03-01

137

Energy demand forecasting by means of Statistical Modelling: Assessing Benefits of Climate Information  

NASA Astrophysics Data System (ADS)

Energy demand forecasting is a critical task and it allows to anticipate any problems that might affect power systems operators, especially during periods with high demand peaks. The difficulties of this task are due to the complexity of the systems involved: energy usage patterns are particularly variable and influenced by many factors, such as weather conditions, social, economic and political aspects (i.e. national regulations, international relations). The strong influence of weather on electricity demand in Italy is due to the wide use of residential air-conditioning devices and, more in general, refrigeration and ventilation equipments. For this reasons, accurate climate information may help in obtaining precise energy demand forecasts, usually performed with statistical methods which show their effectiveness particularly where large amount of data is available. We present a study with the aim of assess the effects of the quality of weather data on statistical modelling performance on energy demand forecasting, using data provided by national transmission grid operator.

De Felice, M.; Alessandri, A.; Ruti, P. M.

2012-04-01

138

Learning from Minimal Economic Models  

Microsoft Academic Search

It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world,\\u000a do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing\\u000a and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue,

Till Grüne-Yanoff

2009-01-01

139

Forecasting long-haul tourism demand for Hong Kong using error correction models  

Microsoft Academic Search

Forecasting accuracy is particularly important when forecasting tourism demand on account of the perishable nature of the product. This study compares a range of forecasting models in the context of predicting annual tourist flows into Hong Kong from the major long-haul markets of the US, the UK, Germany and major short-haul markets of China, Japan and Taiwan. Econometric forecasting models

Koon Nam Lee

2011-01-01

140

Sensitivities of numerical model forecasts of extreme cyclone events  

NASA Astrophysics Data System (ADS)

A global forecast model is used to examine various sensitivities of numerical predictions of three extreme winter storms that occurred near the eastern continental margin of North America: the Ohio Valley blizzard of January 1978, the New England blizzard of February 1978, and the Mid-Atlantic cyclone of February 1979. While medium-resolution simulations capture much of the intensification, the forecasts of the precise timing and intensity levels suffer from various degrees of error. The coastal cyclones show a 5-10 hPa dependence on the western North Atlantic sea surface temperature, which is varied within a range (± 2.5°C ) compatible with interannual fluctuations. The associated vertical velocities and precipitation rates show proportionately stronger dependences on the ocean temperature perturbations. The Ohio Valley blizzard, which intensified along a track 700-800 km from the coast, shows little sensitivity to ocean temperature. The effect of a shift of ˜ 10° latitude in the position of the snow boundary is negligible in each case. The forecasts depend strongly on the model resolution, and the coarse-resolution forecasts are consistently inferior to the medium-resolution forecasts. Studies of the corresponding sensitivities of extreme cyclonic events over eastern Asia are encouraged in order to identify characteristics that are common to numerical forecasts for the two regions.

Yih, A. C.; Walsh, J. E.

1991-03-01

141

A refined fuzzy time-series model for forecasting  

NASA Astrophysics Data System (ADS)

Fuzzy time-series models have been used to model observations, where each one of them contains multiple values. The formulation of fuzzy relationships and the lengths of intervals are considered to be two of the critical factors that affect forecasting results. Unfortunately, the lengths of the intervals were determined during the early stages of forecasting in these models, and they thus often failed to reflect the distribution of observations. This study therefore proposes a refined fuzzy time-series model to further refine the lengths of intervals. This model can refine the lengths of intervals during the formulation of fuzzy relationships, and hence capture the fuzzy relationships more appropriately. As a result, the forecasting results can be improved. Both the stock index and enrollment are used as the targets in the empirical analysis.

Yu, Hui-Kuang

2005-02-01

142

Short-term flood forecasting with a neurofuzzy model  

NASA Astrophysics Data System (ADS)

This study explores the potential of the neurofuzzy computing paradigm to model the rainfall-runoff process for forecasting the river flow of Kolar basin in India. The neurofuzzy computing technique is a combination of a fuzzy computing approach and an artificial neural network technique. Parameter optimization in the model was performed by a combination of backpropagation and least squares error methods. Performance of the neurofuzzy model was comprehensively evaluated with that of independent fuzzy and neural network models developed for the same basin. The values of three performance evaluation criteria, namely, the coefficient of efficiency, the root-mean-square error, and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 hour in advance by the neurofuzzy model. The value of the relative error in peak flow prediction was within reasonable limits for the neurofuzzy model. The neurofuzzy model forecasted 47.95% of the total number of flow values 1 hour in advance with less than 1% relative error, while for the neural network and fuzzy models the corresponding values were 36.96 and 18.89%, respectively. The forecasts by the neurofuzzy model at higher lead times (up to 6 hours) are found to be better than those from the neural network model or the fuzzy model, implying that the neurofuzzy model seems to be well suited to exploit the information to model the nonlinear dynamics of the rainfall-runoff process.

Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

2005-04-01

143

Water balance models in one-month-ahead streamflow forecasting.  

USGS Publications Warehouse

Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. -from Author

Alley, W. M.

1985-01-01

144

Hybrid neural network models for hydrologic time series forecasting  

Microsoft Academic Search

The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination

Ashu Jain; Avadhnam Madhav Kumar

2007-01-01

145

A dynamic manpower forecasting model for the information security industry  

Microsoft Academic Search

Purpose – The purpose of this paper is to develop an integrated model for manpower forecasting for the information security (IS) industry, one of the fastest growing IT-related industries. The proposed model incorporates three critical factors (feedback structure, time lags, and a flexible saturation point) in a system dynamics (SD) simulation frame. Design\\/methodology\\/approach – A simulation model using SD is

Sang-hyun Park; Sang M. Lee; Seong No Yoon; Seung-jun Yeon

2008-01-01

146

Wind power forecasting using advanced neural networks models  

Microsoft Academic Search

In this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of the power output profile of a wind park. This model outperforms simple methods like persistence, as well as classical methods in the literature. The architecture of a forecasting model is optimised automatically by a new algorithm, that substitutes the usually applied

G. N. Kariniotakis; G. S. Stavrakakis; E. F. Nogaret

1996-01-01

147

An integrated stock market forecasting model using neural networks  

Microsoft Academic Search

This paper focuses on the development of a stock market forecasting model based on artificial neural network architecture. A baseline neural network model was developed using GFF architecture. The performance of the baseline model was evaluated by using representative large-cap stocks in six critical industrial sectors. Key performance measures, which included correlation coefficient and mean square error, were identified and

Gary R. Weckman; Sriram Lakshminarayanan; Jon H. Marvel; Andy Snow

2008-01-01

148

Switching ARIMA model based forecasting for traffic flow  

Microsoft Academic Search

Switching dynamic linear models are commonly used methods to describe change in an evolving time series, where the switching ARIMA (autoregressive integrated moving average) model is a special case. Short-term forecasting of traffic flows is an essential part of intelligent traffic systems (ITS). We apply the switching ARIMA model to a traffic flow series. We have observed that the conventional

Guoqiang Yu; Changshui Zhang

2004-01-01

149

Application of mathematical models for flood forecasting in Sri Lanka  

Microsoft Academic Search

With the introduction of micro computers, the application of mathematical models in water resources planning and forecasting became increasingly popular during the last decade in Sri Lanka. The selection of a particular model for a specific river basin was done as far as possible on the basis of an objective criteria to judge the model efficiency. Among the black box

G. T. DHARMASENA

1997-01-01

150

Traffic Accident Macro Forecast Based on ARIMAX Model  

Microsoft Academic Search

To overcome the deficiency of traditional traffic accident estimation models, this paper introduced a new way. It gave two categories on traffic accident affected factors and selected the main ones, using stepwise regression model. ARIMAX model, a dynamic regression one, was used to forecast traffic accident volumes. The former job ensures the precision of estimation, while the latter one owns

Chunyan Li; Jun Chen

2009-01-01

151

The Effects of Age Structure on Economic Growth: An Application of Probabilistic Forecasting in India  

Microsoft Academic Search

During recent years there has been an increasing awareness of the explanatory power of demographic variables in economic growth regressions. We estimate a new model of the effects of age structure change on economic growth. We use the new model and recent probabilistic demographic projections for India to derive the uncertainty of predicted economic growth rates caused by the uncertainty

Alexia Prskawetz; Thomas Kögel; Warren C. Sanderson; Sergei Scherbov

2009-01-01

152

Forecasting  

NSDL National Science Digital Library

This site is a joint effort of NOAA Research and the College of Education at the University of South Alabama. The goal of the site is to provide middle school science students and teachers with research and investigation experiences using on-line resources. In this unit students look at the science of weather forecasting as a science by exploring cloud, temperatures, and air pressure data and information. Students apply this information to interpret and relate meteorological maps to each other. Parts of the unit include gathering information from other websites, applying the data gathered, and performing enrichment exercises. This site contains a downloadable teachers guide, student guide, and all activity sheets to make the unit complete.

153

Modelling and forecasting monthly river discharge considering autoregressive heteroscedasticity  

NASA Astrophysics Data System (ADS)

Monitoring water scarcity conditions requires medium term streamflow forecasting. In this contribution stochastic models for the forecasting of monthly flows were compared. Data measured in monthly time step from the Hron and the Morava Rivers in Slovakia were considered. When analyzing this data in a shorter, daily time step, it was verified, that the from econometry known, so - called heteroscedasticity effect, i.e. the non-constant variance of the time series was present. Here it was investigated, whether this was the case if considering the data with a monthly time step. In addition, the time series were analyzed from two different perspectives: using a purely data driven stochastic model and a hybrid approach, combining physics based conceptual model with a data driven model for the residuals. To model the heteroscedasticity in the time series, the GARCH (generalized autoregressive conditional heteroscedasticity) family of models was fitted to the time series. So far, only a few attempts to apply GARCH class models used on discharge data were reported in the hydrological modelling literature. The goal of investigation was to try to expand the knowledge in the time series modelling of hydrological time series with the aim to test the possibility to use the GARCH family of models on time series with monthly time step and comparing forecasting performance with traditional ARMA models. In order to achieve this, following steps were taken: 1. The presence of heteroscedasticity was verified in time series. 2. An ARMA type model combined with a GARCH model was fitted to the data (either directly on the discharge time series or on the error series resulting from a conceptual model). 3. One - step - ahead forecasts from the fitted models were produced, performing comparisons to forecasts obtained by using only an ARMA class model on the same data. In the case of the purely data driven model it was found, that the medium time step was not fine enough to catch the heteroscedasticity effect, which is present in the data when considering a finer time step at all. Considering the hybrid framework, even though heteroscedasticity was not rejected in the error series, the GARCH family of models did not offer any forecasting improvement compared to the simpler ARMA class of models. This result shows the existence and thus the need of modelling the non-linearities in some cases in the medium step, even if different methods offering better forecasting performance need to be investigated.

Szolgayova, Elena

2010-05-01

154

Battlescale Forecast Model (BFM) During the TFXXI at Fort Irwin, CA: Statistical Evaluation of 24 h Forecast Fields and Model Improvement.  

National Technical Information Service (NTIS)

The U.S. Army's Battlescale Forecast Model (BFM) was operationally used for forecasting surface meteorological parameters during the Department of Defense (DOD) Task Force XXI exercise which was held at the National Training Center (NTC), Ft. Irwin, CA, i...

T. Henmi R. E. Dumais

1998-01-01

155

Forecasting Bankruptcy More Accurately: A Simple Hazard Model  

Microsoft Academic Search

I argue that hazard models are more appropriate than single-period models for forecasting bankruptcy. Single-period models are inconsistent, while hazard models produce consistent estimates. I describe a simple technique for estimating a discrete-time hazard model. I find that about half of the accounting ratios that have been used in previous models are not statistically significant. Moreover, market size, past stock

Tyler Shumway

2001-01-01

156

Fuzzy ARIMA model for forecasting the foreign exchange market  

Microsoft Academic Search

Abstract Considering the time-series ARIMA(p,d,q) model and fuzzy regression model, this paper develops a fuzzy ARIMA (FARIMA) model and applies it to forecasting the exchange rate of NT dollars to US dollars. This model includes interval models with interval parameters and the possibility distribution of future values is provided by FARIMA. This model makes it possible for decision makers to

Fang-mei Tseng; Gwo-hshiung Tzeng; Hsiao-cheng Yu; Benjamin J. C. Yuan

2001-01-01

157

Combined forecast process: Combining scenario analysis with the technological substitution model  

Microsoft Academic Search

Forecasts can be improved by combining separate forecasts obtained by different methods. The complementary nature of the scenario analysis and technological substitution models means that combining the two can obtain improved forecasts. The former has the strength of dealing with the uncertain future, while the later offers data-based forecasts of quantifiable parameters. This study thus proposes a process for combining

Ming-Yeu Wang; Wei-Ting Lan

2007-01-01

158

Precipitation State Forecasting Based on Unascertained CMeans and Markov Chain Model with Gray Relevancy Weights  

Microsoft Academic Search

At present, in the field of hydrology and meteorological science, precipitation state forecasting is an extremely important problem. In this paper, the problem of precipitation state forecasting was studied, and a new forecasting method based unascertained c-means and Markov chain model with gray relevancy weights was presented. The method included the unascertained characteristic of precipitation state comprehensively, thus its forecasting

Li-Hua Ma; Hui-Zhe Yan

2008-01-01

159

Possibility of skill forecast based on the finite-time dominant linear solutions for a primitive equation regional forecast model  

SciTech Connect

The possibility of using forecast errors originating from the finite-time dominant linear modes for the prediction of forecast skill for a primitive equation regional forecast model is studied. This is similar to the method for skill prediction suggested by several other authors using simplified models. Two main problems associated with a sophisticated forecast model not considered in these other studies are investigated: (1) The number of degrees of freedom is typically too large for the evaluation of the spectrum of dominant modes associated with the linear error evolution equation, and (2) many different, physically meaningful, error measures may be used for this model and the dominant linear modes may be sensitive to the selection of the error measure. It is shown first that the finite-time dominant linear solutions can be computed with sufficient accuracy for the complex forecast model using standard [open quotes]power[close quotes] method with a small number of iterations for all error measures considered in this study. The forecast skill is then estimated using the nonlinear forecast errors originating from the initial errors that are defined by these measure-dependent dominant solutions. These results show that the estimated forecast skill is very sensitive to the choice of error measure used for the computation of the finite-time dominant modes. 17 refs., 7 figs.

Vukicevic, T. (National Center for Atmospheric Research, Boulder CO (United States))

1993-06-15

160

USING PPP TO PARALLELIZE OPERATIONAL WEATHER FORECAST MODELS FOR MPPS  

Microsoft Academic Search

The Parallelizing Preprocessor is being developed at t he Forecast Systems Laboratory (FSL) to simplify the process of parallelizing operational weather prediction models for Massively Parallel Processors (MPPs). PPP, a component of FSL's Scalable Modeling System, is a Fortran 77 text analysis and translation tool. PPP directives, implemented as Fortran comments, are inserted into the source c ode. This code

Mark W. Govett; Adwait Sathye; James P. Edwards; Leslie B. Hart

161

Short Term Load Forecasting via a Hierarchical Neural Model  

Microsoft Academic Search

This paper proposes a no vel neural model t o the problem of short t erm load forecasting. The neural model is made up of two self-organizing map nets - one on top o f t he other. It has been successfully applied to domains in which the c ontext i nformation g iven b y former events plays a

Alexandre P. Alves da Silva; Agnaldo J. Rocha Reis

162

Validation of Model Forecasts of the Ambient Solar Wind (Invited)  

Microsoft Academic Search

Independent and automated validation is a vital step in the progression of models from the research community into operational forecasting use. In this paper we describe a program in development at the CCMC to provide just such a comprehensive validation for models of the ambient solar wind in the inner heliosphere. We have built upon previous efforts published in the

P. J. MacNeice; M. Hesse; M. M. Kuznetsova; L. Rastaetter; A. Taktakishvili

2009-01-01

163

Trend time series modeling and forecasting with neural networks  

Microsoft Academic Search

Despite its great importance, there has been no general consensus on how to model the trends in time series data. Compared to traditional approaches, neural networks have shown some promise in time series forecasting. This paper investigates how to best model trend time series using neural networks. Four strategies (raw data, raw data with time index, detrending, and differencing) are

Min Qi; G. Peter Zhang

2003-01-01

164

River flood forecasting with a neural network model  

Microsoft Academic Search

A neural network model was developed to analyze and forecast the behavior of the river Tagliamento, in Italy, during heavy rain periods. The model makes use of distributed rainfall information coming from several rain gauges in the mountain district and predicts the water level of the river at the section closing the mountain district. The water level at the closing

Marina Campolo; Paolo Andreussi; Alfredo Soldati

1999-01-01

165

Performance of a Southern Ocean sea ice forecast model  

Microsoft Academic Search

The presentation examines the forecast peformance of an oriented fracture sea ice model applied to the Southern Ocean to predict sea ice state up to five days in advance. The model includes a modified Coulombic elastic-viscous-plastic rheology, enthalpy conserving thermodynamics and a new method of parameterising thickness distribution mechanics. 15 ice thickness classes are employed within each grid cell with

P. Heil; A. Roberts; W. Budd

2003-01-01

166

Empirical information criteria for time series forecasting model selection  

Microsoft Academic Search

In this article, we propose a new empirical information criterion (EIC) for model selection which penalizes the likelihood of the data by a non-linear function of the number of parameters in the model. It is designed to be used where there are a large number of time series to be forecast. However, a bootstrap version of the EIC can be

Baki Billah; Rob J. Hyndman; Anne B. Koehler

2005-01-01

167

The use of ARIMA models for reliability forecasting and analysis  

Microsoft Academic Search

This paper investigates the approach to repairable system reliability forecasting based on the Autoregressive Integrated Moving Average (ARIMA) models. This time series technique makes very few assumptions and is very flexible. It is theoretically and statistically sound in its foundation and no a priori postulation of models is required when analysing failure data. An illustrative example on a mechanical system

S. L. Ho; M. Xie

1998-01-01

168

The use of HBV model for flash flood forecasting  

Microsoft Academic Search

The standard conceptual HBV model was originally developed with daily data and is normally operated on daily time step. But many floods in Slovenia are usually flash floods as result of intense frontal precipitation combined with orographic enhancement. Peak discharges are maintained only for hours or even minutes. To use the HBV model for flash flood forecasting, the version of

M. Kobold; M. Brilly

2006-01-01

169

Chemical weather forecasting: a new concept of integrated modelling  

Microsoft Academic Search

During the last decade a new field of atmospheric modelling - the chemical weather forecasting (CWF) - is quickly developing and growing. However, in the most of the current studies and publications, this field is considered in a simplified concept of the o -line running chemical transport models with operational numerical weather prediction (NWP) data as a driver. A new

A. Baklanov

2010-01-01

170

Forecasting exposure to volcanic ash based on ash dispersion modeling  

Microsoft Academic Search

A technique has been developed that uses Puff, a volcanic ash transport and dispersion (VATD) model, to forecast the relative exposure of aircraft and ground facilities to ash from a volcanic eruption. VATD models couple numerical weather prediction (NWP) data with physical descriptions of the initial eruptive plume, atmospheric dispersion, and settling of ash particles. Three distinct examples of variations

Rorik A. Peterson; Ken G. Dean

2008-01-01

171

Forecasting comparison between two nonlinear models: fuzzy regression versus SETAR  

Microsoft Academic Search

In this article, we compare the forecasting performances of the Self-Exciting Threshold Autoregressive (SETAR) model and a fuzzy clustering regression model. The series used in this study are high-frequency financial data in the form of seven major stock prices in the US stock markets; the stock indices from seven world stock trading centres; the daily prices for two important commodities,

Hui Feng

2011-01-01

172

Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts  

NASA Astrophysics Data System (ADS)

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

Bao, Hongjun; Zhao, Linna

2012-02-01

173

A hybrid linear-neural model for river flow forecasting  

NASA Astrophysics Data System (ADS)

This paper presents a novel hybrid linear-neural (LN) model formulation to effectively model rainfall-runoff processes. The central idea of the proposed model framework is that the hidden layer of an artificial neural network (ANN) model be designed with a combination of linear and nonlinear neurons. A training algorithm for the proposed model is designed based on minimum description length criteria. The advantage of the algorithm is that the final architecture of the LN model is arrived at during the training process, thus avoiding selection from a class of models. The proposed model has been developed and evaluated for its performance for forecasting the river flow of Kolar basin, in India. The values of three performance evaluation criteria, namely, the coefficient of efficiency, the root-mean-square error, and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 hour in advance by the LN model. The value of the relative error in peak flow prediction was within reasonable limits for the model. The forecasts by the LN model at higher lead times (up to 6 hours) are found to be good. A relative evaluation of LN model performance with that of an ANN model and of a multiple linear regression model indicates that the LN model effectively combines the strength of the other two, implying that the LN model seems to be well suited to exploit the information to model the nonlinear dynamics of the rainfall-runoff process.

Chetan, M.; Sudheer, K. P.

2006-04-01

174

A nonlinear solar cycle model with potential for forecasting on a decadal time scale  

NASA Astrophysics Data System (ADS)

This paper describes a novel nonlinear oscillator model of the sunspot cycle which accurately reproduces several of the observed qualitative and quantitative characteristics of the real cycle including the long term amplitude modulation pattern. The model accounts for 96 percent of cycle peak height variance over the period 1859 to 1980. The aim of this work is to assess the potential of such models for forecasting solar activity on decadal and possibly longer time scales. Longer term forecasts may have practical economic significance because of the growing evidence for relationships between solar cycle variations and terrestrial weather and climatic variations (Bandeen and Moran, 1975; Currie, 1980; Williams, 1981). The model predicts that cycle 22 will have an annual mean peak amplitude in the range 25 to 45, the lowest peak activity for 260 yr.

Gregg, D. P.

1984-01-01

175

Development, testing, and applications of site-specific tsunami inundation models for real-time forecasting  

Microsoft Academic Search

The study describes the development, testing and applications of site-specific tsunami inundation models (forecast models) for use in NOAA's tsunami forecast and warning system. The model development process includes sensitivity studies of tsunami wave characteristics in the nearshore and inundation, for a range of model grid setups, resolutions and parameters. To demonstrate the process, four forecast models in Hawaii, at

L. Tang; V. V. Titov; C. D. Chamberlin

2009-01-01

176

Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts  

EIA Publications

The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

Information Center

2010-06-01

177

A national econometric forecasting model of the dental sector.  

PubMed Central

The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977.

Feldstein, P J; Roehrig, C S

1980-01-01

178

A model for statistical forecasting of menu item demand.  

PubMed

Foodservice planning necessarily begins with a forecast of demand. Menu item demand forecasts are needed to make food item production decisions, work force and facility acquisition plans, and resource allocation and scheduling decisions. As these forecasts become more accurate, the tasks of adjusting original plans are minimized. Forecasting menu item demand need no longer be the tedious and inaccurate chore which is so prevalent in hospital food management systems today. In most instances, data may be easily collected as a by-product of existing activities to support accurate statistical time series predictions. Forecasts of meal tray count, based on a rather sophisticated model, multiplied by average menu item preference percentages can provide accurate predictions of demand. Once the forecasting models for tray count have been developed, simple worksheets can be prepared to facilitate manual generation of the forecasts on a continuing basis. These forecasts can then be recorded on a worksheet that reflects average patient preference percentages (of tray count), so that the product of the percentages with the tray count prediction produces menu item predictions on the same worksheet. As the patient preference percentages stabilize, data collection can be reduced to the daily recording of tray count and one-step-ahead forecase errors for each meal with a periodic gathering of patient preference percentages to update and/or verify the existing date. The author is more thoroughly investigating the cost/benefit relationship of such a system through the analysis of new empirical data. It is clear that the system offers potential for reducing costs at the diet category or total tray count levels. It is felt that these benefits transfer down to the meal item level as well as offer ways of generating more accurate predictions, with perhaps only minor (if any) labor time increments. Research in progress will delineate expected savings more explicitly. The approach requires statistical and computer expertise primarily during the development of the tray count model and patient preference percentage table. The results of this effort can be transferred to a form that is easily utilized by food management personnel manually to generate menu item demand forecasts. PMID:839033

Wood, S D

1977-03-01

179

Distributed models for operational river forecasting: research, development, and implementation  

NASA Astrophysics Data System (ADS)

The National Weather Service (NWS) is uniquely mandated amongst federal agencies to provide river forecasts for the United States. To accomplish this mission, the NWS uses the NWS River Forecast System (NWSRFS). The NWSRFS is a collection of hydrologic, hydraulic, data collection, and forecast display algorithms employed at 13 River Forecast Centers (RFCs) throughout the US. Within the NWS, the Hydrology Lab (HL) of the Office of Hydrologic Development conducts research and development to improve the NWS models and products. Areas of current research include, snow, frozen ground, dynamic channel routing, radar and satellite precipitation estimation, uncertainty, and new approaches to rainfall runoff modeling. A prominent area of research lately has been the utility of distributed models to improve the accuracy of NWS forecasts and to provide meaningful hydrologic simulations at ungaged interior nodes. Current river forecast procedures center on lumped applications of the conceptual Sacramento Soil Moisture Accounting (SAC-SMA) model to transform rainfall to runoff. Unit hydrographs are used to convert runoff to discharge hydrographs at gaged locations. Hydrologic and hydraulic routing methods are used to route hydrographs to downstream computational points. Precipitation inputs to the models have been traditionally defined from rain gage observations. With the nationwide implementation of the Next Generation Radar platforms (NEXRAD), the NWS has precipitation estimates of unprecedented spatial and temporal resolution. In order to most effectively use these high resolution data, recent research has been devoted towards the development of distributed hydrologic models to improve the accuracy of NWS forecasts. The development of distributed models in HL is following specific scientific research and implementation strategies, each consisting of several elements. In its science strategy, HL has conducted a highly successful comparison of distributed models (Distributed Model Intercomparison Project- DMIP) in order to identify which model or process algorithms would benefit the NWS mission. DMIP has also been designed to understand issues such as the use of operational data, the amount of calibration required, and methods of deriving initial parameter estimates. DMIP has garnered participation from 12 research institutions in the US and abroad, including China, Canada, and Denmark. Simultaneously, HL has developed a flexible modeling system that can be used to develop and evaluate various rainfall runoff models and modeling approaches (gridded distributed, semi distributed, and lumped). HL has successfully participated in DMIP with a gridded distributed model consisting of the SAC-SMA and kinematic routing in each computational element. As a result of DMIP, the NWS has decided to move ahead with the implementation of the HL distributed model. As with the research effort, a specific implementation plan is being followed. First, a prototype version of the research distributed model is being run at the one RFC for real time operations. Short term software development is being conducted to make this research version more user friendly. Long term software development is planned to derive a system to efficiently support operational distributed modeling. Long term research will also continue into new rainfall/runoff/routing models and well as parameter estimation, calibration and state updating issues. Formal implementation includes a transition phase in which the new distributed model will be run parallel to the current lumped model in selected basins, providing the forecaster with two simulations for decision making. Moreover, such a transition period will provide much needed exposure and training. Problems identified to date with the deployment of distributed models include the addition of a snow model, issues relating to the quality of the NEXRAD data, methods of parameterizing and calibrating a distributed model, methods of state updating, and training of personnel in the transition from lumped to distributed model forecasting.

Smith, M.

2003-04-01

180

A univariate model for long-term streamflow forecasting  

Microsoft Academic Search

This paper, the second in the series, verifies the entropy-based univariate model developed in the first paper for long-term streamflow forecasting on five rivers from different regions of the world. The results of the model are compared with the corresponding results of ARIMA and state-space model. The Lagrange multipliers of the univariate model are found similar to autocorrelation coefficients of

P. F. Krstanovic; V. P. Singh

1991-01-01

181

Trend analysis model to forecast energy supply and demand  

SciTech Connect

A particular approach to energy forecasting which was studied in considerable detail was trend extrapolation. This technique, termed the trend analysis model, was suggested by Dr. S. Scott Sutton, the EIA contract technical officer. While a variety of equations were explored during this part of the study, they are variations of a basic formulation. This report describes the trend analysis model, demonstrates the trend analysis model and documents the computer program used to produce the model results.

Not Available

1984-01-01

182

Macroeconomic forecasts and microeconomic forecasters  

Microsoft Academic Search

In the presence of principal-agent problems, published macroeconomic forecasts by professional economists may not measure expectations. Forecasters may use their forecasts in order to manipulate beliefs about their ability. I test a cross-sectional implication of models of reputation and information-revelation. I find that as forecasters become older and more established, they produce more radical forecasts. Since these more radical forecasts

Owen A. Lamont

2002-01-01

183

Networking Sensor Observations, Forecast Models & Data Analysis Tools  

NASA Astrophysics Data System (ADS)

This presentation explores the interaction between sensor webs and forecast models and data analysis processes within service oriented architectures (SOA). Earth observation data from surface monitors and satellite sensors and output from earth science models are increasingly available through open interfaces that adhere to web standards, such as the OGC Web Coverage Service (WCS), OGC Sensor Observation Service (SOS), OGC Web Processing Service (WPS), SOAP-Web Services Description Language (WSDL), or RESTful web services. We examine the implementation of these standards from the perspective of forecast models and analysis tools. Interoperable interfaces for model inputs, outputs, and settings are defined with the purpose of connecting them with data access services in service oriented frameworks. We review current best practices in modular modeling, such as OpenMI and ESMF/Mapl, and examine the applicability of those practices to service oriented sensor webs. In particular, we apply sensor-model-analysis interfaces within the context of wildfire smoke analysis and forecasting scenario used in the recent GEOSS Architecture Implementation Pilot. Fire locations derived from satellites and surface observations and reconciled through a US Forest Service SOAP web service are used to initialize a CALPUFF smoke forecast model. The results of the smoke forecast model are served through an OGC WCS interface that is accessed from an analysis tool that extract areas of high particulate matter concentrations and a data comparison tool that compares the forecasted smoke with Unattended Aerial System (UAS) collected imagery and satellite-derived aerosol indices. An OGC WPS that calculates population statistics based on polygon areas is used with the extract area of high particulate matter to derive information on the population expected to be impacted by smoke from the wildfires. We described the process for enabling the fire location, smoke forecast, smoke observation, and population statistics services to be registered with the GEOSS registry and made findable through the GEOSS Clearinghouse. The fusion of data sources and different web service interfaces illustrate the agility in using standard interfaces and help define the type of input and output interfaces needed to connect models and analysis tools within sensor webs.

Falke, S. R.; Roberts, G.; Sullivan, D.; Dibner, P. C.; Husar, R. B.

2009-12-01

184

Mass Balance and Economic Models.  

National Technical Information Service (NTIS)

Mass balance is not a sophisticated scientific theory but simply a method of accounting. Mass balance is discussed in general terms as a procedure to construct models comparable in all disciplines, including economics. The relation between the generalized...

J. R. Marsden D. E. Pingry A. Whinston

1973-01-01

185

A study on wind energy generation forecasting using connectionist models  

Microsoft Academic Search

Wind generation is the most widespread form of renewable energy, with a high degree of penetration in traditional electricity networks. Hence, the influence of wind power generation over the efficient operation of power systems is increasingly complex. This determines the actors playing in the wind energy market to show an increased interest in developing efficient forecasting models for power generated

Iulia Coroama; Mihai Gavrilas

2010-01-01

186

Bayesian Hierarchical Models to Augment the Mediterranean Ocean Forecast System.  

National Technical Information Service (NTIS)

The first full year of research for the project entitled 'Bayesian Hierarchical Models (BHM) to Augment the Mediterranean Ocean Forecast System (MFS)' completed at the end of May 2006. Project achievements have met or exceeded plans put forth in the propo...

R. Milliff

2006-01-01

187

Weather load model for electric demand and energy forecasting  

Microsoft Academic Search

A method of forecasting the heat sensitive portion of electrical demand and energy utilizing a summer weather load model and taking into account probability variation of weather factors is discussed in this paper. The heat sensitive portion of the load is separated from base load and historical data is used to determine the effect of weather on the system load.

C. E. Asbury

1975-01-01

188

A neural network model for forecasting fish stock recruitment  

Microsoft Academic Search

A neural network model is developed to forecast the recruiting biomass of fish. The west coast of Vancouver Island, British Columbia, Pacific herring (Clupea pallasi) stock is selected as an example application based on data compiled from long-term ecosystem research and stock assessment programs. A fuzzy logic decision procedure was used to evaluate all possible neural networks. The output from

D. G. Chen; D. M. Ware

1999-01-01

189

Structural breaks, ARIMA model and Finnish inflation forecasts  

Microsoft Academic Search

Via the use of the rolling regression technique and a specific procedure for analysing strong structural breaks in a univariate time series model, we forecast the rate of future inflation in Finland for the time period of unregulated financial markets since the beginning of 1987. The identified structural changes in the data generating process (DGP) of inflation are labelled with

Juha Junttila

2001-01-01

190

Forecasting gaming revenues in Clark County, Nevada: Issues and methods.  

National Technical Information Service (NTIS)

This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. It is designed to generate forecasts of gaming revenues in Clark County, wh...

B. K. Edwards A. Bando

1992-01-01

191

Stochastic vs. conceptual models for river flow forecasting  

NASA Astrophysics Data System (ADS)

The paper compares two models for river flow forecasting at monthly and daily scale recalling the connections between stochastic and conceptual models pointed out by a number of different authors in the past. The models are different in that one belongs to the class of stochastic models of the ARMA family and the other to the class of hybrid metric-conceptual models which combine the use of observations (the metric paradigm) and other prior knowledge with the testing of hypotheses about the structure of component hydrological stores (the conceptual paradigm) at catchment scale. More in detail, the stochatic model is an ARMAX model (Autoregressive Moving average with Exogenous input), that has been already been employed for real time forecasting of streamflows at daily scale and snowmelt runoff and the conceptual model is the IAHCRES (Identification of Hydrographs And Components from Rainfall Evaporation and Streamflow), a conceptual model that allows flexible schematizations of both surface and groundwater flow by combining channels and reservoirs in different ways using a conceptual parsimonious and not over-parameterised. The study aims at highlighting the different performances of the two types of models when applied to forecasting river flows of Alcantara river, a 400 km2 catchment in eastern Sicily with a significant groundwater component.

Arena, C.; Aronica, G. T.; Franza, F.

2009-04-01

192

Coherent mortality forecasting: the product-ratio method with functional time series models.  

PubMed

When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary time series models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations. PMID:23055234

Hyndman, Rob J; Booth, Heather; Yasmeen, Farah

2013-02-01

193

Intelligent forecasting system based on grey model and neural network  

Microsoft Academic Search

This paper presents the design issues of two intelligent forecasting systems, feedforward-neural-network-aided grey model (FNAGM) and Elman-network-aided grey model (ENAGM). Both he FNAGM and ENAGM combine a first-order single variable grey model (GM(1,1)) and a neural network (NN). The GM(1,1) is adopted to predict signal, and the feedforward NN and the Elman network in the FNAGM and ENAGM respectively are

Shih-Hung Yang; Yon-Ping Chen

2009-01-01

194

Constraints on Rational Model Weighting, Blending and Selecting when Constructing Probability Forecasts given Multiple Models  

NASA Astrophysics Data System (ADS)

Ensemble forecasting on a lead time of seconds over several years generates a large forecast-outcome archive, which can be used to evaluate and weight "models". Challenges which arise as the archive becomes smaller are investigated: in weather forecasting one typically has only thousands of forecasts however those launched 6 hours apart are not independent of each other, nor is it justified to mix seasons with different dynamics. Seasonal forecasts, as from ENSEMBLES and DEMETER, typically have less than 64 unique launch dates; decadal forecasts less than eight, and long range climate forecasts arguably none. It is argued that one does not weight "models" so much as entire ensemble prediction systems (EPSs), and that the marginal value of an EPS will depend on the other members in the mix. The impact of using different skill scores is examined in the limits of both very large forecast-outcome archives (thereby evaluating the efficiency of the skill score) and in very small forecast-outcome archives (illustrating fundamental limitations due to sampling fluctuations and memory in the physical system being forecast). It is shown that blending with climatology (J. Bröcker and L.A. Smith, Tellus A, 60(4), 663-678, (2008)) tends to increase the robustness of the results; also a new kernel dressing methodology (simply insuring that the expected probability mass tends to lie outside the range of the ensemble) is illustrated. Fair comparisons using seasonal forecasts from the ENSEMBLES project are used to illustrate the importance of these results with fairly small archives. The robustness of these results across the range of small, moderate and huge archives is demonstrated using imperfect models of perfectly known nonlinear (chaotic) dynamical systems. The implications these results hold for distinguishing the skill of a forecast from its value to a user of the forecast are discussed.

Higgins, S. M. W.; Du, H. L.; Smith, L. A.

2012-04-01

195

Multi-model Ensembling based on Predictor State Space: Seasonal Streamflow Forecasts and Causal Relations  

Microsoft Academic Search

Seasonal streamflow forecasts contingent on climate information are essential for short-term planning and for setting up contingency measures during extreme years. Recent research show that operational climate forecasts obtained from multiple General Circulation Models (GCM) have improved predictability than climate forecasts from single GCMs. In this study, we present a new approach for multi-model ensembling by evaluating model performance from

S. Arumugam; N. Devineni; S. Ghosh

2006-01-01

196

1993 Pacific Northwest Loads and Resources Study, Pacific Northwest Economic and Electricity Use Forecast, Technical Appendix: Volume 1.  

SciTech Connect

This publication documents the load forecast scenarios and assumptions used to prepare BPA`s Whitebook. It is divided into: intoduction, summary of 1993 Whitebook electricity demand forecast, conservation in the load forecast, projection of medium case electricity sales and underlying drivers, residential sector forecast, commercial sector forecast, industrial sector forecast, non-DSI industrial forecast, direct service industry forecast, and irrigation forecast. Four appendices are included: long-term forecasts, LTOUT forecast, rates and fuel price forecasts, and forecast ranges-calculations.

United States. Bonneville Power Administration.

1994-02-01

197

A flood routing Muskingum type simulation and forecasting model based on level data alone  

NASA Astrophysics Data System (ADS)

While the use of remote hydrometers for measuring the level in water courses is both economical and widespread, the same cannot be said for cross section and channel profile measurements and, even less, for rating curves at the measuring cross sections, all of which are more often than not incomplete, out of date, and unreliable. The mass of data involved in level measurements alone induces a degree of perplexity in those who try to use them, for example, for flood event simulations or the construction of forecasting models which are not purely statistical. This paper proposes a method which uses recorded level data alone to construct a simulation model and a forecasting model, both of them characterized by an extremely simple structure that can be used on any pocket calculator. These models, referring to a river reach bounded by two measuring sections, furnish the downstream levels, where the upstream levels are known, and the downstream level at time t + ?t*, where the upstream and downstream levels are known at time t, respectively. The numerical applications performed show that while the simulation model is somewhat penalized by the simplifications adopted, giving not consistently satisfactory results on validation, the forecasting model generated good results in all the cases examined and seems reliable.

Franchini, Marco; Lamberti, Paolo

1994-07-01

198

Review of Wind Energy Forecasting Methods for Modeling Ramping Events  

SciTech Connect

Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

2011-03-28

199

Modelling and Forecasting in the Dry Bulk Shipping Market  

Microsoft Academic Search

This dissertation proposes strategies not only for modelling price behavior in the dry bulk market, but also for modelling relationships between economic and technical variables of dry bulk ships, by using modern time series approaches, Monte Carlo simulation and other economic techniques. The time series modelling techniques, described extensively in Appendix A, primarily consist of the Vector Error Correction model

S. Chen

2011-01-01

200

Towards operational modeling and forecasting of the Iberian shelves ecosystem.  

PubMed

There is a growing interest on physical and biogeochemical oceanic hindcasts and forecasts from a wide range of users and businesses. In this contribution we present an operational biogeochemical forecast system for the Portuguese and Galician oceanographic regions, where atmospheric, hydrodynamic and biogeochemical variables are integrated. The ocean model ROMS, with a horizontal resolution of 3 km, is forced by the atmospheric model WRF and includes a Nutrients-Phytoplankton-Zooplankton-Detritus biogeochemical module (NPZD). In addition to oceanographic variables, the system predicts the concentration of nitrate, phytoplankton, zooplankton and detritus (mmol N m(-3)). Model results are compared against radar currents and remote sensed SST and chlorophyll. Quantitative skill assessment during a summer upwelling period shows that our modelling system adequately represents the surface circulation over the shelf including the observed spatial variability and trends of temperature and chlorophyll concentration. Additionally, the skill assessment also shows some deficiencies like the overestimation of upwelling circulation and consequently, of the duration and intensity of the phytoplankton blooms. These and other departures from the observations are discussed, their origins identified and future improvements suggested. The forecast system is the first of its kind in the region and provides free online distribution of model input and output, as well as comparisons of model results with satellite imagery for qualitative operational assessment of model skill. PMID:22666349

Marta-Almeida, Martinho; Reboreda, Rosa; Rocha, Carlos; Dubert, Jesus; Nolasco, Rita; Cordeiro, Nuno; Luna, Tiago; Rocha, Alfredo; Lencart E Silva, João D; Queiroga, Henrique; Peliz, Alvaro; Ruiz-Villarreal, Manuel

2012-05-29

201

Critique of the mid-range energy forecasting, system oil and gas supply models  

SciTech Connect

The Mid-Range Energy Forecasting System (MEFS) is a model used by the Department of Energy to forecast domestic production, consumption and price for conventional energy sources on a regional basis over a period of 5 to 15 years. Among the energy sources included in the model are oil, gas and other petroleum fuels, coal, uranium, and electricity. Final consumption of alternative energy sources is broken into end-use categories, such as residential, commercial and industrial uses. Regional prices for all energy sources are calculated by iteratively equating domestic supply and demand. The purpose of this paper is to assess the ability of the Oil and Gas Supply Submodels of MEFS to reliably and accurately project oil and gas supply curves, which are used in the integrating model, along with fuel demand curves to estimate market price. The reliability and accuracy of the oil and gas model cannot be judged by comparing its predictions against actual observations because those observations have not yet occurred. The reliability and reasonableness of the oil and gas supply model can be judged, however, by analyzing how well its assumptions and predictions correspond to accepted economic principles. This is the approach taken in this critique. The remainder of this paper describes the general structure of the oil and gas supply model and how it functions to project the quantity of oil and gas forthcoming at given prices in a particular year, then discusses the economic soundness of the model, and finally suggests model changes to improve its performance.

Patton, W.P.

1980-10-01

202

Monthly and seasonal streamflow forecasts using rainfall-runoff modeling and historical weather data  

Microsoft Academic Search

Rainfall-runoff models can reliably quantify catchment initial conditionsCatchment states and resampled historical rainfall enable skillful streamflow forecastWhole ensemble of historical forcings leads to the best streamflow forecasts

Enli Wang; Yongqiang Zhang; Jiangmei Luo; Francis H. S. Chiew; Q. J. Wang

2011-01-01

203

Forecasting of Annual Streamflow Using Data-Driven Modeling Approach  

NASA Astrophysics Data System (ADS)

In a water-stressed region, such as the western United States, it is essential to have long lead-time streamflow forecast for reservoir operation and water resources management. In this study, we develop and examine the accuracy of a data driven model incorporating large-scale climate information for extending streamflow forecast lead-time. A data driven model i.e. Support Vector Machine (SVM) based on the statistical learning theory is used to predict annual streamflow volume 1-year in advance. The SVM model is a learning system that uses a hypothesis space of linear functions in a Kernel induced higher dimensional feature space, and is trained with a learning algorithm from the optimization theory. Annual oceanic-atmospheric indices, comprising of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), El Niño-Southern Oscillations (ENSO), and a new Sea Surface Temperature (SST) data set of “Hondo” region for a period of 1906-2006 are used to generate annual streamflow volumes for multiple sites in Gunnison River Basin (GRB) and San Juan River Basin (SJRB) located in the Upper Colorado River Basin (UCRB). Based on Correlation Coefficient, Root Means Square Error, and Mean Absolute Error the model shows satisfactory results, and the predictions are in good agreement with measured streamflow volumes. Previous research has identified NAO and ENSO as main drivers for extending streamflow forecast lead-time in the UCRB. Contrary to this, the current research shows a stronger signal between the “Hondo” region SST and GRB and SJRB streamflow for 1-year lead-time. Streamflow predictions from the SVM model are found to be better when compared with the predictions obtained from feed-forward back propagation Artificial Neural Network model and Multiple Linear Regression model. The streamflow forecast provide valuable and useful information for optimal management and planning of water resources in the basins.

Kalra, A.; Miller, W. P.; Ahmad, S.; Lamb, K. W.

2010-12-01

204

A FCM-based deterministic forecasting model for fuzzy time series  

Microsoft Academic Search

The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness inherent in the data collected. A variety of forecasting models including high-order models have been devoted to improving forecasting accuracy. However, the high-order forecasting approach is accompanied by the crucial problem of determining an appropriate order number. Consequently, such

Sheng-Tun Li; Yi-Chung Cheng; Su-Yu Lin

2008-01-01

205

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models  

Microsoft Academic Search

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over

V. Lara-Fanego; D. Pozo-Vazquez; J. A. Ruiz-Arias; F. J. Santos-Alamillos; J. Tovar-Pescador

2010-01-01

206

A transactions choice model for forecasting demand for alternative-fuel vehicles  

Microsoft Academic Search

The vehicle choice model developed here is one component in a micro-simulation demand forecasting system being designed to produce annual forecasts of new and used vehicle demand by vehicle type and geographic area in California. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by time of day for electric vehicles. The choice model

David Brownstone; David S. Bunch; Thomas F. Golob; Weiping Ren

1996-01-01

207

Long-term wind speed and power forecasting using local recurrent neural network models  

Microsoft Academic Search

This paper deals with the problem of long-term wind speed and power forecasting based on meteorological information. Hourly forecasts up to 72-h ahead are produced for a wind park on the Greek island of Crete. As inputs our models use the numerical forecasts of wind speed and direction provided by atmospheric modeling system SKIRON for four nearby positions up to

Thanasis G. Barbounis; John B. Theocharis; Minas C. Alexiadis; Petros S. Dokopoulos

2006-01-01

208

Real-Business-Cycle Models and the Forecastable Movements in Output, Hours, and Consumption  

Microsoft Academic Search

The authors study the movements in output, consumption, and hours that are forecastable from a vector autoregression and analyze how they differ from those predicted by standard real-business-cycle models. They show that actual forecastable movements in output have a variance about one hundred times larger than those predicted by the model. The authors also find that forecastable changes in the

Julio J. Rotemberg; Michael Woodford

1996-01-01

209

Forecasting precipitation for hydrological modeling in Alpine watersheds  

NASA Astrophysics Data System (ADS)

Precipitation fields from numerical weather prediction models are the main input in hydrolocial modeling of flood events. Here, precipitation forecasts at different spatial resolutions (4 to 40 km) from the limited--area model (LAM) ALADIN are considered. The target areas are selected Alpine watersheds with areas between 2600 and 7300 square--kilometers. Within these watersheds the quality of precipitation forecasts at different spatial resolutions is investigated using direct and indirect means of validation. Emphasize is given to the indirect validation of precipitation against observed runoff using appropriate surface schemes. In addition, schematic validation is possible through comparison of accumulated rainfall and observed runoff on an event basis. These approaches are applied to rainfall events within the Mesoscale Alpine Programme Special Observation Period (MAP SOP, Sept 7. to Nov 15., 1999).

Beck, A.; Ahrens, B.

2003-04-01

210

Models for forecasting energy use in the US farm sector  

NASA Astrophysics Data System (ADS)

Econometric models were developed and estimated for the purpose of forecasting electricity and petroleum demand in US agriculture. A structural approach is pursued which takes account of the fact that the quantity demanded of any one input is a decision made in conjunction with other input decisions. Three different functional forms of varying degrees of complexity are specified for the structural cost function, which describes the cost of production as a function of the level of output and factor prices. Demand for materials (all purchased inputs) is derived from these models. A separate model which break this demand up into demand for the four components of materials is used to produce forecasts of electricity and petroleum is a stepwise manner.

Christensen, L. R.

1981-07-01

211

Residential construction demand forecasting using economic indicators: a comparative study of artificial neural networks and multiple regression  

Microsoft Academic Search

In recent years, demand for residential construction has been growing rapidly in Singapore. This paper proposes the use of economic indicators to predict demand for residential construction in Singapore. At the same time, two forecasting techniques are applied, namely, Artificial Neural Networks (ANN) and Multiple Regression (MR), the former being a state-of-the-art technique while the latter a conventional one. A

Goh Bee Hua

1996-01-01

212

Daily reservoir inflow forecasting combining QPF into ANNs model  

NASA Astrophysics Data System (ADS)

Daily reservoir inflow predictions with lead-times of several days are essential to the operational planning and scheduling of hydroelectric power system. The demand for quantitative precipitation forecasting (QPF) is increasing in hydropower operation with the dramatic advances in the numerical weather prediction (NWP) models. This paper presents a simple and an effective algorithm for daily reservoir inflow predictions which solicits the observed precipitation, forecasted precipitation from QPF as predictors and discharges in following 1 to 6 days as predicted targets for multilayer perceptron artificial neural networks (MLP-ANNs) modeling. An improved error back-propagation algorithm with self-adaptive learning rate and self-adaptive momentum coefficient is used to make the supervised training procedure more efficient in both time saving and search optimization. Several commonly used error measures are employed to evaluate the performance of the proposed model and the results, compared with that of ARIMA model, show that the proposed model is capable of obtaining satisfactory forecasting not only in goodness of fit but also in generalization. Furthermore, the presented algorithm is integrated into a practical software system which has been severed for daily inflow predictions with lead-times varying from 1 to 6 days of more than twenty reservoirs operated by the Fujian Province Grid Company, China.

Zhang, Jun; Cheng, Chun-Tian; Liao, Sheng-Li; Wu, Xin-Yu; Shen, Jian-Jian

2009-01-01

213

Comparing Models for Forecasting the Yield Curve  

Microsoft Academic Search

The evolution of the yields of different maturities is related and can be described by a reduced number of commom latent factors. Multifactor interest rate models of the finance literature, common factor models of the time series literature and others use this property. Each model has advantages and disadvantages, and it is an empirical matter to evaluate the performance of

Marco S. Matsumura; Ajax R. B. Moreira

2006-01-01

214

Based on dynamic regression model medium and long-term demand forecast and analysis of China's electric power  

Microsoft Academic Search

Based on comprehensive analysis of the current methods on medium and long-term prediction of electric power demand, a new model-dynamic regression model is represented to forecast and analyze China's electricity demands. Combined with time series method and multivariate regression method, this model can be more comprehensive, reasonable to reflect the operational laws and long-term trends of electric economic system. Through

Liu Nan

2010-01-01

215

Economic Benefits to Agriculture in the San Joaquin Valley Due to Long-Term Prediction Streamflow Forecasting.  

National Technical Information Service (NTIS)

A mathematical model of a surface water and ground water system is developed to study agricultural benefits for various uncertainties of long-range streamflow forecasting. Both optimization and simulation techniques were used to solve the problem consider...

M. J. Cohn

1978-01-01

216

Real-Time Flood Forecasting System Using Channel Flow Routing Model with Updating by Particle Filter  

Microsoft Academic Search

A real-time flood forecasting system using channel flow routing model was developed for runoff forecasting at water gauged and ungaged points along river channels. The system is based on a flood runoff model composed of upstream part models, tributary part models and downstream part models. The upstream part models and tributary part models are lumped rainfall-runoff models, and the downstream

R. Kudo; H. Chikamori; A. Nagai

2008-01-01

217

The skill of precipitation and surface temperature forecasts by the NMC global model during DERF II. [NMC (National Meteorological Center) DERF II (Dynamical Extended Range Forecast II)  

Microsoft Academic Search

This study assesses the skill of forecasts of precipitation and surface temperature by the National Meteorological Center's (NMC) global model in the 108 consecutive 30-day forecasts (known as Dynamical Extended Range Forecast II (DERF II)) that were made from initial conditions 24 h apart between 14 December 1986 and 31 March 1987. Model precipitation accumulated during the first 24 h

G. H. White; E. Kalnay; R. Gardner; M. Kanamitsu

1993-01-01

218

A high resolution forecast model of storm surge inundation  

NASA Astrophysics Data System (ADS)

In order to forecast storm surge inundation, a two-dimensional model is established. In the model, an alternating computation sequence method is used to solve the governing equations, and the dry and wet method is introduced to treat the moving boundary. This model is easy to use. It has a friendly input interface and Arcview GIS is used as the output interface. The model is applied to the Shantou area to simulate the storm surge elevations and inundations caused by Typhoons 6903 ane 0104 using the same relevant parameters. The calculated results agree well with the observations.

Liu, Juan; Jiang, Wensheng; Sun, Wenxin; Wang, Yongzhi

2005-04-01

219

A hybrid linear-neural model for time series forecasting.  

PubMed

This paper considers a linear model with time varying parameters controlled by a neural network to analyze and forecast nonlinear time series.We show that this formulation, called neural coefficient smooth transition autoregressive (NCSTAR) model, is in close relation to the threshold autoregressive (TAR) model and the smooth transition autoregressive (STAR) model with the advantage of naturally incorporating linear multivariate thresholds and smooth transitions between regimes. In our proposal, the neuralnetwork output is used to induce a partition of the input space, with smooth and multivariate thresholds. This also allows the choice of good initial values for the training algorithm. PMID:18249864

Medeiros, M C; Veiga, A

2000-01-01

220

Application of data assimilation to solar wind forecasting models  

NASA Astrophysics Data System (ADS)

Data Assimilation through Kalman filtering [1,2] is a powerful statistical tool which allows to combine modeling and observations to increase the degree of knowledge of a given system. We apply this technique to the forecast of solar wind parameters (proton speed, proton temperature, absolute value of the magnetic field and proton density) at 1 AU, using the model described in [3] and ACE data as observations. The model, which relies on GOES 12 observations of the percentage of the meridional slice of the sun covered by coronal holes, grants 1-day and 6-hours in advance forecasts of the aforementioned quantities in quiet times (CMEs are not taken into account) during the declining phase of the solar cycle and is tailored for specific time intervals. We show that the application of data assimilation generally improves the quality of the forecasts during quiet times and, more notably, extends the periods of applicability of the model, which can now provide reliable forecasts also in presence of CMEs and for periods other than the ones it was designed for. Acknowledgement: The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under the grant agreement N. 218816 (SOTERIA project: http://www.soteria-space.eu). References: [1] R. Kalman, J. Basic Eng. 82, 35 (1960); [2] G. Welch and G. Bishop, Technical Report TR 95-041, University of North Carolina, Department of Computer Science (2001); [3] B. Vrsnak, M. Temmer, and A. Veronig, Solar Phys. 240, 315 (2007).

Innocenti, M.; Lapenta, G.; Vrsnak, B.; Temmer, M.; Veronig, A.; Bettarini, L.; Lee, E.; Markidis, S.; Skender, M.; Crespon, F.; Skandrani, C.; Soteria Space-Weather Forecast; Data Assimilation Team

2010-12-01

221

Using Artificial Market Models to Forecast Financial Time-Series  

Microsoft Academic Search

We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself -- was discussed by Johnson et al. in cond-mat\\/0105303 and cond-mat\\/0105258 and

Nachi Gupta; Raphael Hauser; Neil F. Johnson

2005-01-01

222

Parsimonious modeling and forecasting of corporate yield curve  

Microsoft Academic Search

This paper investigates the sensitivity of out-of-sample forecasting performance over a span of different parameters of l in the dynamic Nelson-Siegel three-factor AR(1) model. First, we find that the ad hoc selection of l is not optimal. Second, we find a substantial difference in factor dynamics between investment-grade and speculative-grade corporate bonds from 1994:12 to 2006: 4. Third, we suggest

Wei-Choun Yu; Donald M. Salyards

2009-01-01

223

Further Development of a 3-7 Day Typhoon Analog Forecast Model for the Western North Pacific.  

National Technical Information Service (NTIS)

The report presents the results of an investigation of typhoon movement forecasting techniques by computer. The study was accomplished in three phases: Upgrading the former data base of the existing forecast model; extending the forecast to seven days, an...

W. S. Yogi J. M. Long J. F. Steuckert

1975-01-01

224

An Econometric Forecasting Model of the United States. The MCL Macroeconomic Model.  

National Technical Information Service (NTIS)

The Mathematics and Computation Laboratory (MCL) Macroeconomic Model is designed to forecast the gross national product (GNP) and its components in the United States. Both static and dynamic simulations are conducted to test the model. The report provides...

T. T. Su

1979-01-01

225

A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates  

Microsoft Academic Search

Abstract In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear auto- regression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ame- liorate forecasting performances. We compare,the new model’s performance,with the two individual forecasting models—GLAR and ANN—as well as with the hybrid model and the linear combination models. Empirical results

Lean Yu; Shouyang Wang; Kin Keung Lai

2005-01-01

226

An automatic leading indicator of economic activity: forecasting GDP growth for European countries  

Microsoft Academic Search

In the construction of a leading indicator model of economic activity, economists must select among a pool of variables which lead output growth. Usually the pool of variables is large and a selection of a subset must be carried out. This paper proposes an automatic leading indicator model which, rather than preselection, uses a dynamic factor model to summarize the

GONZALO CAMBA-MENDEZ; GEORGE KAPETANIOS; RICHARD J. SMITH; MARTIN R. WEALE

2001-01-01

227

Economic Assessment of a Concentrating Solar Power Forecasting System for Participation in the Spanish Electricity Market  

Microsoft Academic Search

Forecasts of power production are necessary for the electricity market participation of Concentrating Solar Power (CSP) plants. Deviations from the production schedule may lead to penalty charges. the mitigation impact on deviation penalties of an electricity production forecasting tool for Therefore, the accuracy of direct normal irradiance (DNI) forecasts is an important issue. This paper elaborates the 50 MWel parabolic

Birk Kraas; Marion Schroedter-Homscheidt; Benedikt Pulvermüller; Reinhard Madlener

2011-01-01

228

Economic Analysis. Computer Simulation Models.  

ERIC Educational Resources Information Center

A multimedia course in economic analysis was developed and used in conjunction with the United States Naval Academy. (See ED 043 790 and ED 043 791 for final reports of the project evaluation and development model.) This volume of the text discusses the simulation of behavioral relationships among variable elements in an economy and presents…

Sterling Inst., Washington, DC. Educational Technology Center.

229

Generation of ensemble streamflow forecasts using an enhanced version of the snowmelt runoff model  

Technology Transfer Automated Retrieval System (TEKTRAN)

As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1-15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt-dominated ...

230

Forecasting Monthly Sales of Cellphone Companies - the Use of VAR Model  

Microsoft Academic Search

Forecast of earnings is one of the major tasks for financial statement analysts, and sales forecast is the most important step in the process of predicting earnings. Taking one large cell phone company in Taiwan as example and by the use of monthly data to fit a proper VAR model, we found that, when forecasting monthly sales, number of clients

Hsueh-Fang Chien; Shu-Hua Lee; Lee Wen-chih; Yann-ching Tsai

2007-01-01

231

Forecast Verification for Eta Model Winds Using Lake Erie Storm Surge Water Levels  

Microsoft Academic Search

This article has two purposes. The first is to describe how the Great Lakes Coastal Forecasting System (GLCFS) can be used to validate wind forecasts for the Great Lakes using observed and forecast water levels. The second is to evaluate how well two versions (40 km and 29 km) of the numerical weather prediction step-coordinate Eta Model are able to

William P. O’Connor; David J. Schwab; Gregory A. Lang

1999-01-01

232

The Tropical Cyclone Modeling Team (TCMT): Evaluation of Experimental Models for Tropical Cyclone Forecasting in Support of the NOAA Hurricane Forecast Improvement Project (HFIP)  

Microsoft Academic Search

In 2009, the National Center for Atmospheric Science (NCAR)\\/Research Applications Laboratory's (RALs) Joint Numerical Testbed (JNT) Program formed a new entity called the Tropical Cyclone Modeling Team (TCMT). The focus of this team is testing and evaluation of experimental models with the goal of improving tropical cyclone forecasts. Much of this effort is sponsored by NOAA's Hurricane Forecast Improvement Project

P. A. Kucera; B. Brown; L. B. Nance; K. M. Crosby; C. Williams; T. Jensen

2010-01-01

233

Distributed models for operational river forecasting: research, development, and implementation  

Microsoft Academic Search

The National Weather Service (NWS) is uniquely mandated amongst federal agencies to provide river forecasts for the United States. To accomplish this mission, the NWS uses the NWS River Forecast System (NWSRFS). The NWSRFS is a collection of hydrologic, hydraulic, data collection, and forecast display algorithms employed at 13 River Forecast Centers (RFCs) throughout the US. Within the NWS, the

M. Smith

2003-01-01

234

Modeling and forecasting the information sciences  

Microsoft Academic Search

Abstract A model of the development,of the information sciences is described and used to account for past events and predict future trends, particularly fifth and sixth generation priorities. The information sciences came into prominence,as electronic device technology enabled the social need to cope with an increasingly complex,world to be satisfied. Underlying all developments,in computing is a tiered succession of learning

Brian R. Gaines

1991-01-01

235

Application Study of Empirical Model and Xiaohuajian Flood Forecasting Model in the Middle Yellow River  

NASA Astrophysics Data System (ADS)

Xiaolandi-Huayuankou region is an important rainstorm centre in the middle Yellow river, which drainage area of 35883km2. A set of forecasting methods applied in this region was formed throughout years of practice. The Xiaohuajian flood forecasting model and empirical model were introduced in this paper. The simulated processes of the Xiaohuajian flood forecasting model include evapotranspiration, infiltration, runoff, river flow. Infiltration and surface runoff are calculated utilizing the Horton model for infiltration into multilayered soil profiles. Overland flow is routed by Nash instantaneous unit hydrograph and Section Muskingum method. The empirical model are simulated using P~Pa~R and empirical relation approach for runoff generation and concentration. The structures of these two models were analyzed and compared in detail. Yihe river basin located in Xiaolandi-Huayuankou region was selected for the purpose of the study. The results show that the accuracy of the two methods are similar, however, the accuracy of Xiaohuajian flood forecasting model for flood forecasting is relatively higher, especially the process of the flood; the accuracy of the empirical methods is much worse, but it can also be accept. The two models are both practicable, so the two models can be combined to apply. The result of the Xiaohuajian flood forecasting model can be used to guide the reservoir for flood control, and the result of empirical methods can be as a reference.

Hu, Caihong

2013-04-01

236

Multisite seasonal forecast of arid river flows using a dynamic model combination approach  

NASA Astrophysics Data System (ADS)

This paper dynamically combines three independent forecasts of multiple river flow volumes a season in advance for arid catchments. The case study considers five inflow locations in the upper Namoi Catchment of eastern Australia. The seasonal flows are predicted on the basis of concurrent sea surface temperature anomalies (SSTAs), which are predicted a season forward using a dynamic combination of three SSTA forecasts. The river flows are predicted using three statistical forecasting models: (1) a mixture of generalized lognormal and multinomial logit models, (2) the local regression of independent components of five inflows, and (3) the weighted nearest neighbor method, where each of these models use the forecasted SSTA along with prior lags of the flow as the main driving variables. The study demonstrates that improved SSTA forecast (due to dynamic combination) in turn improves all three flow forecasts, while the dynamic combination of the three flow forecasts results in further, although smaller, improvements.

Chowdhury, Shahadat; Sharma, Ashish

2009-10-01

237

On the Causes of the Poor Simulation and Forecast of the Intraseasonal Oscillation by Numerical Models  

NASA Astrophysics Data System (ADS)

Many attempts to simulate and forecast the Intraseasonal Oscillation (ISO) using numerical models have met with difficulties. In this work we present results from a series of extended forecasting simulations conducted jointly between Georgia Tech and the ECMWF aimed at improving our understanding of the problems that numerical models have in simulating and forecasting the ISO. This study evaluates the skill of a numerical model in simulating the processes that occur during the transition from suppressed to active convection which is considered key for skillful extended forecasts in the Indo-West Pacific region. Regional and local vertical structure of ISO-related anomalies from the numerical forecasts using the ECMWF model are compared to those in the ERA-40 data during different stages of the convective activity (suppressed, transition, and active). This analysis explores ISO numerical simulations during the TOGA COARE winter case, as well ISO events during the summers of 2002 and 2004. Results suggest that the skill of the model forecasting the vertical structure of the ISO strongly depends on the atmospheric thermodynamic state at the beginning of each forecast run. In addition, there are states of the system for which the skill of the forecast is always low associated with convective events for which the skill of the forecast decreases regardless of the starting date of the forecast. The forecast skill of circulation anomalies is higher than the skill of moist convective associated anomalies. The time scale of skillful forecasts during summer is half of that obtained for winter, indicating that the skill of the forecast is greater for winter ISO cases than for summer events. Analyses of the summer simulations indicate that the model is always predicting an active-like phase of the monsoon. Since the model is not able to forecast skillfully the generation of specific humidity anomalies in the equatorial Indian Ocean, convective anomalies do not propagate from the equator resulting in the lack of intraseasonal modulation of the monsoon.

Agudelo, P. A.; Curry, J. A.; Hoyos, C. D.; Webster, P. J.

2007-12-01

238

Traffic congestion forecasting model for the INFORM System. Final report  

SciTech Connect

This report describes a computerized traffic forecasting model, developed by Brookhaven National Laboratory (BNL) for a portion of the Long Island INFORM Traffic Corridor. The model has gone through a testing phase, and currently is able to make accurate traffic predictions up to one hour forward in time. The model will eventually take on-line traffic data from the INFORM system roadway sensors and make projections as to future traffic patterns, thus allowing operators at the New York State Department of Transportation (D.O.T.) INFORM Traffic Management Center to more optimally manage traffic. It can also form the basis of a travel information system. The BNL computer model developed for this project is called ATOP for Advanced Traffic Occupancy Prediction. The various modules of the ATOP computer code are currently written in Fortran and run on PC computers (pentium machine) faster than real time for the section of the INFORM corridor under study. The following summarizes the various routines currently contained in the ATOP code: Statistical forecasting of traffic flow and occupancy using historical data for similar days and time (long term knowledge), and the recent information from the past hour (short term knowledge). Estimation of the empirical relationships between traffic flow and occupancy using long and short term information. Mechanistic interpolation using macroscopic traffic models and based on the traffic flow and occupancy forecasted (item-1), and the empirical relationships (item-2) for the specific highway configuration at the time of simulation (construction, lane closure, etc.). Statistical routine for detection and classification of anomalies and their impact on the highway capacity which are fed back to previous items.

Azarm, A.; Mughabghab, S.; Stock, D.

1995-05-01

239

Agrometeorology and models for the parasite cycle forecast.  

PubMed

Insects are strongly influenced by meteorological variables in their natural environment. In agriculture, mathematical models have been developed to understand and forecast the cycle of pests based on climate data. By this manner, with the goal of reduce and rationalize plant chemical treatments, agrometeorological models have been realized to estimate the length and starting times of parasites phenological phases. In Sicily a new network of 95 GSM meteorological stations and a specific mathematical model for Aonidiella aurantii are used by Sicilian Agrometeorological Information System (SIAS) for the integrated pest management program of citrus orchards in the Island. As the plants parasites, vector borne diseases are influenced by climate in their appearance and abundance. In lights of the benefits that could derive from a model for the control of Leishmania vectors, SIAS experiences in modelling were used to develop a deductive model for Phlebotomus perniciosus which represents the major vector of human and canine leishmaniasis in Sicily. PMID:16881403

Pasotti, L; Maroli, M; Giannetto, S; Brianti, E

2006-06-01

240

A composite approach to forecasting state government revenues: Case study of the Idaho sales tax  

Microsoft Academic Search

Fiscal problems have led to increased reliance on economic and revenue forecasting by state governments in recent years. As a means of improving accuracy, many forecasters use alternative outlooks. Composite modeling goes a step further and allows analysts to systematically combine two or more forecasts. This paper examines the effectiveness of composite forecasting of sales tax revenues in Idaho. Base

Thomas Fullerton Jr.

1989-01-01

241

Linked space physics models for operational ionospheric forecasting  

NASA Astrophysics Data System (ADS)

The shorter-term variable impact of the Sun's photons, solar wind particles, and interplanetary magnetic field upon the Earth's environment that can adversely affect technological systems is colloquially known as space weather. It includes, for example, the effects of solar coronal mass ejections, solar flares and irradiances, solar and galactic energetic particles, as well as the solar wind, all of which affect Earth's magnetospheric particles and fields, geomagnetic and electrodynamical conditions, radiation belts, aurorae, ionosphere, and the neutral thermosphere and mesosphere. These combined effects create risks to space and ground systems from electric field disturbances, irregularities, and scintillation, for example, where these ionospheric perturbations are a direct result of space weather. A major challenge exists to improve our understanding of ionospheric space weather processes and then translate that knowledge into operational systems. Ionospheric perturbed conditions can be recognized and specified in real-time or predicted through linkages of models and data streams. Linked systems must be based upon multi-spectral observations of the Sun, solar wind measurements by satellites between the Earth and Sun, as well as by measurements from radar and GPS/TEC networks. Models of the solar wind, solar irradiances, the neutral thermosphere, thermospheric winds, joule heating, particle precipitation, substorms, the electric field, and the ionosphere provide climatological best estimates of non-measured current and forecast parameters. We report on a team effort that is developing a prototype operational ionospheric forecast system to detect and predict the conditions leading to dynamic ionospheric changes. The system will provide global-to-local specifications of recent history, current epoch, and 72-hour forecast ionospheric and neutral density profiles, TEC, plasma drifts, neutral winds, and temperatures. Geophysical changes will be captured and/or predicted (modeled) at their relevant time scales ranging from 10-minute to hourly cadences. 4-D ionospheric densities are being specified using data assimilation techniques, coupled with physics-based and empirical models for thermospheric, solar, electric field, particle, and magnetic field parameters that maximize accuracy in locales and regions at the current epoch, maintain global self-consistency, and improve reliable forecasts. We report on a system architecture underlying the linkage of models and data streams that is operationally reliable and robust to serve commercial space weather needs.

Tobiska, W.; Bouwer, D.; Forbes, J.; Frahm, R.; Fry, C.; Hagan, M.; Hajj, G.; Hsu, T.; Knipp, D.; Mannucci, A.; Papitashvili, V.; Pi, X.; Sharber, J.; Storz, M.; Wang, C.; Wilson, B.

2003-12-01

242

Mesoscale Forecasts Generated from Operational Numerical Weather-Prediction Model Output  

Microsoft Academic Search

A technique called Model Output Enhancement (MOE) has been developed for the generation and display of mesoscale weather forecasts. The MOE technique derives mesoscale or high-resolution (order of 1 km) weather forecasts from synoptic-scale numerical weather-prediction models by modifying model output with geophysical and land-cover data. Mesoscale forecasts generated by the MOE technique are displayed as color-class maps overlaid on

John G. W. Kelley; Joseph M. Russo; J. Ronald Eyton; Toby N. Carlson

1988-01-01

243

Combined model for PM10 forecasting in a large city  

NASA Astrophysics Data System (ADS)

We present the results of a PM10 forecasting model that has been applied for air quality management in Santiago, Chile during recent years. The daily operation of this model has served to inform in advance to the population about the air quality they will find in different areas of the city and to help environmental authorities in the decision to take actions on days when concentrations are in ranges considered significantly harmful and to impose restrictions to the activity of the city in advance, when extreme episodes are foreseen. At present, national PM10 standard for 24 h average is 150 ?g m-3. According to the range where the concentrations fall, five levels or classes of air quality are defined: good (A), regular (B), bad (C), Critical (D) and Emergency (E). Forecasting is based on the combination of artificial neural networks and a nearest neighbor method. Inputs to the models are concentrations measured at several monitoring stations distributed throughout the city and meteorological information in the region. Outputs are the expected maxima concentrations for the following day at the site of the same monitoring stations. Results for last three years (2009, 2010, 2011) indicate that the model may be considered as an important tool for air pollution control.

Perez, Patricio

2012-12-01

244

Are Spanish Ibex35 stock future index returns forecasted with non-linear models?  

Microsoft Academic Search

This study employs different nonlinear models (smooth transition autoregressive models (STAR), artificial neural networks (ANN) and nearest neighbours (NN)) to study the predictability of one-step-ahead forecast returns for the Ibex35 stock future index at a one year forecast horizon. It is found that the STAR, ANN and NN models beat the random walk (RW) and linear autoregressive (AR) models in

Jorge V. Pérez-Rodríguez; Salvador Torra; Julian Andrada-Félix

2005-01-01

245

Forecasting Dust Storms Using the CARMA-Dust Model and MM5 Weather Data  

Microsoft Academic Search

An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5th generation Penn State Mesoscale Meteorology Model (MM5), and a modified version of the Colorado Aerosol and Radiation Model for Atmospheres (CARMA). AFWA conducted

B. H. Barnum; N. S. Winstead; J. Wesely; A. Hakola; P. Colarco; O. B. Toon; P. Ginoux; G. Brooks; L. M. Hasselbarth; B. Toth; R. Sterner

2002-01-01

246

Forecasting dust storms using the CARMA-dust model and MM5 weather data  

Microsoft Academic Search

An operational model for the forecast of dust storms in Northern Africa, the Middle East and Southwest Asia has been developed for the United States Air Force Weather Agency (AFWA). The dust forecast model uses the 5th generation Penn State Mesoscale Meteorology Model (MM5) as input to the University of Colorado CARMA dust transport model. AFWA undertook a 60 day

Benjamin H. Barnum; Nathaniel S. Winstead; Jeremy J. Wesely; Amy R. Hakola; Peter R. Colarco; Owen B. Toon; Paul Ginoux; Gordon R. Brooks; Linda M. Hasselbarth; Bruce A. Toth

2004-01-01

247

Modeling and Forecasting the Malawi Kwacha-US Dollar Nominal Exchange Rate  

Microsoft Academic Search

This study develops a blended version of the monetary and portfolio models for the MK\\/USD exchange rate, and assesses the forecasting performance of the model against a simple random walk. The results indicate that the model performs better than the simple random walk on the 6, 12 and 24 months forecasting horizons. However, the model does not perform well on

Kisu Simwaka

2007-01-01

248

Global Financial\\/Economic Crisis and Tourist Arrival Forecasts for Hong Kong  

Microsoft Academic Search

This paper examines the impact of the global financial\\/economic crisis on the demand for Hong Kong tourism by residents of 10 major source markets for the period 2009–2012. To capture the influence of this crisis, the Autoregressive Distributed Lag Model (ADLM) is used to calculate the demand elasticities, and four scenarios (ranging from the most pessimistic to the most optimistic)

Haiyan Song; Shanshan Lin; Xinyan Zhang; Zixuan Gao

2010-01-01

249

MJO empirical modeling and improved prediction by "Past Noise Forecasting"  

NASA Astrophysics Data System (ADS)

The Madden-Julian oscillation (MJO) is the dominant mode of intraseasonal variability in tropics and plays an important role in global climate. Here we presents modeling and prediction study of MJO by using Empirical Model Reduction (EMR). EMR is a methodology for constructing stochastic models based on the observed evolution of selected climate fields; these models represent unresolved processes as multivariate, spatially correlated stochastic forcing. In EMR, multiple polynomial regression is used to estimate the nonlinear, deterministic propagator of the dynamics, as well as multi-level additive stochastic forcing -"noise", directly from the observational dataset. The EMR approach has been successfully applied on the seasonal-to-interannual time scale for real-time ENSO prediction (Kondrashov et al. 2005), as well as atmospheric midlatitude intraseasonal variability (Kondrashov et al. 2006,2010). In this study nonlinear (quadratic) with annual cycle, three-level EMR model was developed to model and predict leading pair of real-time multivariate Madden-Julian oscillation (RMM1,2) daily indices (June 1974- January 2009, http://cawcr.gov.au/staff/mwheeler/maproom/RMM/). The EMR model captures essential MJO statistical features, such as seasonal dependence, RMM1,2 autocorrelations and spectra. By using the "Past Noise Forecasting" (PNF) approach developed and successfully applied to improve long-term ENSO prediction in Chekroun et al. (2011), we are able to notably improve the cross-validated prediction skill of RMM indices- especially at lead times of 15-to-30 days. The EMR/PNF method has two steps: (i) select noise samples - or "snippets" - from the past noise, which have forced the EMR model to yield the MJO phase resembling the one at the the currently observed state; and (ii) use these "noise" snippets to create ensemble forecast of EMR model. The MJO phase identification is based on Singular Spectrum Analysis reconstruction of 30-60 day MJO cycle.

Kondrashov, D. A.; Chekroun, M.; Robertson, A. W.; Ghil, M.

2011-12-01

250

A comparative verification of forecasts from two operational solar wind models  

NASA Astrophysics Data System (ADS)

The solar wind (SW) and interplanetary magnetic field (IMF) have a significant influence on the near-Earth space environment. In this study we evaluate and compare forecasts from two models that predict SW and IMF conditions: the Hakamada-Akasofu-Fry (HAF) version 2, operational at the Air Force Weather Agency, and Wang-Sheeley-Arge (WSA) version 1.6, executed routinely at the Space Weather Prediction Center. SW speed (Vsw) and IMF polarity (Bpol) forecasts at L1 were compared with Wind and Advanced Composition Explorer satellite observations. Verification statistics were computed by study year and forecast day. Results revealed that both models' mean Vsw are slower than observed. The HAF slow bias increases with forecast duration. WSA had lower Vsw forecast-observation difference (F-O) absolute means and standard deviations than HAF. HAF and WSA Vsw forecast standard deviations were less than observed. Vsw F-O mean square skill rarely exceeds that of recurrence forecasts. Bpol is correctly predicted 65%-85% of the time in both models. Recurrence beats the models in Bpol skill in nearly every year forecast day category. Verification by "event" (flare events ?5 days before forecast start) and "nonevent" (no flares) forecasts showed that most HAF Vsw bias growth, F-O standard deviation decrease, and forecast standard deviation decrease were due to the event forecasts. Analysis of single time step Vsw increases of ?20% in the nonevent forecasts indicated that both models predicted too many occurrences and missed many observed incidences. Neither model had skill above a random guess in predicting Vsw increase arrival time at L1.

Norquist, Donald C.; Meeks, Warner C.

2010-12-01

251

Wind and Load Forecast Error Model for Multiple Geographically Distributed Forecasts  

SciTech Connect

The impact of wind and load forecast errors on power grid operations is frequently evaluated by conducting multi-variant studies, where these errors are simulated repeatedly as random processes based on their known statistical characteristics. To generate these errors correctly, we need to reflect their distributions (which do not necessarily follow a known distribution law), standard deviations, auto- and cross-correlations. For instance, load and wind forecast errors can be closely correlated in different zones of the system. This paper introduces a new methodology for generating multiple cross-correlated random processes to simulate forecast error curves based on a transition probability matrix computed from an empirical error distribution function. The matrix will be used to generate new error time series with statistical features similar to observed errors. We present the derivation of the method and present some experimental results by generating new error forecasts together with their statistics.

Makarov, Yuri V.; Reyes Spindola, Jorge F.; Samaan, Nader A.; Diao, Ruisheng; Hafen, Ryan P.

2010-11-02

252

Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales)  

NASA Astrophysics Data System (ADS)

The Delft Flood Early Warning System provides a versatile framework for real-time flood forecasting. The UK Environment Agency has adopted the Delft framework to deliver its National Flood Forecasting System. The Delft system incorporates new flood forecasting models very easily using an "open shell" framework. This paper describes how we added the data-based mechanistic modelling approach to the model inventory and presents a case study for the Eden catchment (Cumbria, UK).

Leedal, D.; Weerts, A. H.; Smith, P. J.; Beven, K. J.

2013-01-01

253

Modelling standard cost commitment curves for contractors' cash flow forecasting  

Microsoft Academic Search

Cash flow forecasting and control are essential to the survival of any contractor. The time available for a detailed pre-tender cash flow forecast is often limited. Therefore, contractors require simpler and quicker techniques which would enable them to forecast cash flow with reasonable accuracy. This paper identifies causes behind the inaccuracy of current standard value S-curves (which are often used

A. P. Kaka; A. D. F. Price

1993-01-01

254

Forecasting without historical data: Bayesian probability models utilizing expert opinions  

Microsoft Academic Search

The need for forecasting without historical data arises in many circumstances. In some circumstances one may wish to forecast recent and unique events where there are no available historic patterns. In other situations there may be potentially useful past trends rendered irrelevant to the present forecast because the environment has changed fundamentally. In addition, even if there is clear and

Jeffrey F. Driver; Farrokh Alemi

1995-01-01

255

Seasonal Ensemble Forecasting with Ocean General Circulation Model in the Baltic Sea  

NASA Astrophysics Data System (ADS)

Ensemble forecasts are a promising new approach to numerous applications in oceanography. They have for long been an essential tool in meteorology. In marine environment, there is a possibility of even further development, in large part due to the longer predictability. This may, e.g., mean more accurate long-term forecasts for oceanographic parameters. In this work we used the ensemble approach to seasonal forecasting of physical and chemical changes during spring bloom in Baltic Sea. We present results of an ensemble forecasting in the Baltic, and discuss the applicability of this method to operational biogeochemical ocean modelling. FMI's operational 3-dimensional biogeochemical model was used to produce monthly ensemble forecasts for different physical, chemical and biological variables. The modelled variables were temperature, salinity, velocity, silicate, phosphate, nitrate, diatoms, flagellates and two species of potentially toxic filamentous cyanobacteria. Ensembles were produced by running several 30 day runs of the biogeochemical model. The model was forced every run with different set of seasonal weather parameters from ECMWF's mathematically perturbed ensemble prediction forecasts. The ensembles were then analysed by statistical methods and the median, quartiles, minimum and maximum values were calculated for model output variables to gain insight into the applicability of the results. Validation for the forecast method was made by comparing the results against in-situ data. The results of the model demonstrated that ensemble forecasting is a viable tool and it is indeed possible to forecast with useful accuracy the Baltic Sea with these time spans.

Roiha, P.; Westerlund, A.; Stipa, T.

2009-04-01

256

Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models  

Microsoft Academic Search

This paper uses the dynamic factor model framework, which accommodates a large cross-section of macroeconomic time series, for forecasting regional house price inflation. In this study, we forecast house price inflation for five metropolitan areas of South Africa using principal components obtained from 282 quarterly macroeconomic time series in the period 1980:1 to 2006:4. The results, based on the root

Sonali Das; Rangan Gupta; Alain Kabundi

2011-01-01

257

Improving Federal-Funds Rate Forecasts in VAR Models Used for Policy Analysis  

Microsoft Academic Search

Federal-funds rate-forecast errors from vector autoregressive (VAR) models used for monetary policy analysis and fitted by ordinary least squares (OLS) are large relative to those from the futures market. Using three different structural VAR models, we show that forecasts based on a shrinkage estimator dominate the OLS-based forecasts--even after restricting the lag length and\\/or imposing exact unit-root restrictions--and are broadly

John C Robertson; Ellis W Tallman

2001-01-01

258

Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain)  

Microsoft Academic Search

In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric model for Andalusia (southern Spain). GHI forecasts were produced directly by the model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and

V. Lara-Fanego; J. A. Ruiz-Arias; D. Pozo-Vázquez; F. J. Santos-Alamillos; J. Tovar-Pescador

259

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam  

Microsoft Academic Search

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results\\u000a in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes\\u000a crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based\\u000a on stochastic modeling or artificial intelligence (AI)

Ahmed El-Shafie; Mahmoud Reda Taha; Aboelmagd Noureldin

2007-01-01

260

Day-ahead electricity price forecasting using the wavelet transform and ARIMA models  

Microsoft Academic Search

This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow,

Antonio J. Conejo; Miguel A. Plazas; Rosa Espínola; Ana B. Molina

2005-01-01

261

Customer short term load forecasting by using ARIMA transfer function model  

Microsoft Academic Search

Short-term load forecasting plays an important role in electric power system operation and planning. An accurate load forecasting not only reduces the generation cost in a power system, but also provides a good principle of effective operation. In this paper, the ARIMA model and transfer function model are applied to the short-term load forecasting by considering weather-load relationship. For four

M. Y. Cho; J. C. Hwang; C. S. Chen

1995-01-01

262

Day-ahead wind speed forecasting using f-ARIMA models  

Microsoft Academic Search

With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed\\/power forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of fractional-ARIMA or f-ARIMA models to model, and forecast wind speeds on the day-ahead (24h) and two-day-ahead (48h)

Rajesh G. Kavasseri; Krithika Seetharaman

2009-01-01

263

A two-stage dynamic sales forecasting model for the fashion retail  

Microsoft Academic Search

The difficulty with fashion retail forecasting is due to a number of factors such as the season, region and fashion effect and causes a nonlinear change in the original sales rules. To improve the accuracy of fashion retail forecasting, a two-stage dynamic forecasting model is proposed, which is combined with both long-term and short-term predictions. The model introduces the improved

Yanrong Ni; Feiya Fan

2011-01-01

264

Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?  

Microsoft Academic Search

The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center

F. Dominguez; C. L. Castro

2009-01-01

265

A STREAMFLOW FORECASTING FRAMEWORK USING MULTIPLE CLIMATE AND HYDROLOGICAL MODELS1  

Microsoft Academic Search

Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial condi- tions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formi- dable challenge. Recent enhancements in climate forecasting

Paul J. Block; Francisco Assis Souza Filho; Liqiang Sun; Hyun-Han Kwon

266

Application of tank, NAM, ARMA and neural network models to flood forecasting  

Microsoft Academic Search

Two lumped conceptual hydrological models, namely tank and NAM and a neural network model are applied to flood forecasting in two river basins in Thailand, the Wichianburi on the Pasak River and the Tha Wang Pha on the Nan River using the flood forecasting procedure developed in this study. The tank and NAM models were calibrated and verified and found

Tawatchai Tingsanchali; Mahesh Raj Gautam

2000-01-01

267

Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts  

Microsoft Academic Search

A voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using ARCH and stochastic volatility models. While most of these studies have documented highly significant in-sample parameter estimates and pronounced intertemporal volatility persistence, traditional ex post forecast evaluation criteria suggest that the models provide seemingly poor volatility forecasts. Contrary to this contention, the authors show that

Torben G. Andersen; Tim Bollerslev

1998-01-01

268

Modern Project Management: A New Forecasting Model to Ensure Project Success  

Microsoft Academic Search

This paper presents a new forecasting model to ensure project success. This new model is based on improvement on earned value (EV) method. This model improves earned value management system (EVMS) and forecasting time and cost for projects. These method use established consists of four variables: scheduled performance index (SPI), cost performance index (CPI), cost variance (CV), schedule variance (SV).

Iman Attarzadeh

2009-01-01

269

N-order difference heuristic model of fuzzy time series forecasting  

Microsoft Academic Search

Fuzzy time series forecasting model is an effective method to solve the nonlinear problems forecasting. However, most published fuzzy time series based models did not count the change trend implicit in historical datum. In this paper, authors proposed a novel method which applied heuristic information to the fuzzy time series model based on Fibonacci sequence. As an example, the USD\\/JPY

Chi Kai; Che Wen-Gang

2009-01-01

270

A Capacity Forecast Model for Volatile Data in Maintenance Logistics  

NASA Astrophysics Data System (ADS)

Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintaining valuable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network. The article outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning model. This enables a quick response to maintenance orders by employing appropriate measures. The information gained through the model is then systematically applied to forecast both personnel capacities and the demand for spare parts. The improved planning reliability can support MRO service providers in shortening delivery times and reducing stock levels in order to enhance the performance of their maintenance logistics.

Berkholz, Daniel

2009-05-01

271

Nowcasting of precipitation - Advective statistical forecast model (SAM) for the Czech Republic  

NASA Astrophysics Data System (ADS)

This article describes a model SAM for nowcasting precipitation in the Czech Republic. The model is based on an advective-statistical algorithm and utilises radar, satellite, lightning and numerical weather prediction (NWP) model prognostic data. The statistical part of the model is complemented by a distribution correction of forecasted values. The model provides forecasts for mean 1-h, 2-h and 3-h precipitation totals in 9 km by 9 km grid cells. The formulation of the forecast serves the needs of operational hydrologic prediction by the Czech Hydrometeorological Institute (CHMI). The accuracy of the forecasts is evaluated with independent data by the root-mean-square error, absolute error, bias, and the critical success index (CSI) categorical skill score after transforming the quantitative forecasts into categorical forecasts. Results are also compared with the existing operational forecasts by the COTREC model calculated at the CHMI.The results of this study confirmed that the most important predictors are derived from radar data and that, in addition to rain rates, the predictors include information about the top of the radar echo. For the 2-h and 3-h precipitation totals, satellite and NWP model predictors were selected, whereas predictors derived from lightning data were not selected. Evaluation of the forecasts showed that the SAM model yielded more accurate predictions than the COTREC model for almost all forecast accuracy statistics considered. The SAM model does not change the horizontal structure of the COTREC forecasts, because both models use almost identical advection fields. However, the SAM is able to model precipitation development and, thus, improve the forecast accuracy.

Sokol, Zbynek; Pesice, Petr

2012-01-01

272

A stochastic HMM-based forecasting model for fuzzy time series.  

PubMed

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast. PMID:20028637

Li, Sheng-Tun; Cheng, Yi-Chung

2009-12-18

273

Development of a Limited-Area Model for Operational Weather Forecasting around a Power Plant: The Need for Specialized Forecasts  

Microsoft Academic Search

A hydrostatic meteorological model, `PMETEO,' was developed for short-range forecasts for a high-resolution limited area located in the northwest region of Spain. Initial and lateral boundary conditions are externally provided by a coarse-mesh model that has much poorer horizontal and vertical resolution than the fine PMETEO grid. Limitations of limited-area models due to lateral boundary conditions are widely known, given

C. F. Balseiro; M. J. Souto; E. Penabad; J. A. Souto; V. Pérez-Muñuzuri

2002-01-01

274

A Type 2 fuzzy time series model for stock index forecasting  

NASA Astrophysics Data System (ADS)

Most conventional fuzzy time series models (Type 1 models) utilize only one variable in forecasting. Furthermore, only part of the observations in relation to that variable are used. To utilize more of that variable's observations in forecasting, this study proposes the use of a Type 2 fuzzy time series model. In such a Type 2 model, extra observations are used to enrich or to refine the fuzzy relationships obtained from Type 1 models and then to improve forecasting performance. The Taiwan stock index, the TAIEX, is used as the forecasting target. The study period extends over the 2000 2003 period. The TAIEX from January to October in each year is used for the estimation, while that covering November and December is used for forecasting. The empirical analyses show that Type 2 model outperforms Type 1 model.

Huarng, Kunhuang; Yu, Hui-Kuang

2005-08-01

275

Comparison of Dst forecast models for intense geomagnetic storms  

NASA Astrophysics Data System (ADS)

We have compared six Dst forecast models using 63 intense geomagnetic storms (Dst ? -100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (?Dstmin) and the difference in the absolute value of Dst minimum time (?tDst) between the observed and the predicted. As a result, we found that the model by Temerin and Li (2002, 2006) gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the ?Dstmin of 7.7 nT, and the absolute value of ?tDst of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Temerin and Lee (2002, 2006) is better than the other models for the events having -100 ? Dst < -200 nT, and three recent models (the model of Wang et al. (2003), the model of Temerin and Li (2002, 2006), and the model of Boynton et al. (2011b)) are better than the other three models for the events having Dst ? -200 nT.

Ji, Eun-Young; Moon, Y.-J.; Gopalswamy, N.; Lee, D.-H.

2012-03-01

276

IRI-2001 model efficiency in ionospheric radiowave propagation forecasting  

NASA Astrophysics Data System (ADS)

Results of data analysis referring to two type of experimental investigations are presented: (i) oblique chirp-sounding (OS) on two perpendicular paths: Inskip(GB)-IZMIRAN (˜2500 km) and Cyprus-IZMIRAN (˜2300 km) and (ii) vertical sounding at their common point (IZMIRAN). These investigations were performed in the vernal equinox periods of 2002-2007 covering like that a wide range of solar activity, for example, the mean sunspot number for March varied from 112.0 at the maximum (2002) to 4.6 at the minimum (2007). In order to improve the quality of the short-term radio wave propagation forecasting, an attempt was made to consider this combined set of experimental data from the point of view of its description made by IRI-2001 empirical model, and to examine a proposed procedure to adapt the model to the current geophysical conditions. The aim of the paper was to quantify the root-mean-square (RMS) error for two different descriptions of the behavior of maximum usable frequency (MUF) 1F2 mode: long-term - monthly averaged dependence and short-term - diurnal dynamics. These estimates are obtained as in the case of classic way of the model input parameters assignment (monthly mean sunspot number) so when solar radio flux data was considered as an additional information about the solar activity. Special emphasis is focused on the estimation of the accuracy of the short-term description, when current solar radio flux and F2-layer peak data is used as input parameters for the IRI-2001 model in the proposed procedure of its adaptation. It is shown that relative RMS error in 10% is the bottom estimation of an accuracy of MUF 1F2 in the long- and short-term forecasts, at least, for March. This limitation, apparently, is caused by a considerable discrepancy between the F1-layer electron density values obtained from the model and from the observed N(h)-profiles.

Krasheninnikov, I. V.; Egorov, I. B.

2010-01-01

277

Linking a mesoscale atmospheric model to a catchment model for flood forecasting in New Zealand  

NASA Astrophysics Data System (ADS)

New Zealand is a land drained by short steep rivers that rise rapidly in response to heavy rainfall (more than 10,000 mm/y in parts of the Southern Alps). Flood forecasting systems that use only measured upstream flows or water levels provide limited flood warning lead times. For many New Zealand rivers there is a need for longer flood warning lead times if flood damage is to be reduced. We report on the feasibility of using meso-scale precipitation model forecasts as input to rainfall-to-runoff models, for the purpose of increasing the lead times for flood forecasts. We have linked the output of the RAMS mesoscale atmospheric model to Topnet, a semi-distributed rainfall-runoff-routing model based around TOPMODEL and kinematic wave routing in a river network. The reliability of the method has been tested on the extensive set of detailed meteorological and hydrological measurements made as part of the 1996 Southern Alps Experiment (SALPEX), in 23 catchments of varying size along both flanks of the Southern Alps. The testing used precipitation generated on a 20 km grid and provided a succession of 48 hour ahead runoff forecasts on a daily basis. The results of the tests indicated that useful runoff forecasts were produced by the combined system and so the development of an operational forecasting system is now in progress. The rainfall produced from the initial mesoscale model had a significant bias (underestimation): further investigation has shown that this was caused by (i) unresolved subgrid variability (a 5km grid is needed) and (ii) an inappropriate cloud physics parameterisation. We report on the resolution of these issues, and our progress in extending the spatial coverage of the hydrological models, with the eventual goal of making national flood forecasts for thousands of river reaches.

Ibbitt, R. P.; Henderson, R. D.; Woods, R. A.; Gray, W. G.; Turner, R.

2003-04-01

278

An Elementary Mathematical Model for the Interpretation of Precipitation Probability Forecasts  

Microsoft Academic Search

A mathematical model is constructed for interpreting precipitation probability forecasts which have areal connotations. The paper is chiefly concerned with a simple discrete form of the model, in which a forecast area is represented by N rain gages scattered throughout it, and a precipitation event is identified with the observing of more than a trace of precipitation in a particular

J. H. Curtiss

1968-01-01

279

Statistical applications of physically based hydrologic models to seasonal streamflow forecasts  

Microsoft Academic Search

Despite advances in physically based hydrologic models and prediction systems, long-standing statistical methods remain a fundamental component in most operational forecasts of seasonal streamflow. We develop a hybrid framework that employs gridded observed precipitation and model-simulated snow water equivalent (SWE) data as predictors in regression equations adapted from an operational forecasting environment. We test the modified approach using the semidistributed

Eric A. Rosenberg; Andrew W. Wood; Anne C. Steinemann

2011-01-01

280

Using High Resolution Numerical Weather Prediction Models to Reduce and Estimate Uncertainty in Flood Forecasting  

Microsoft Academic Search

Forecast rainfall from Numerical Weather Prediction (NWP) and\\/or nowcasting systems is a major source of uncertainty for short-term flood forecasting. One approach for reducing and estimating this uncertainty is to use high resolution NWP models that should provide better rainfall predictions. The potential benefit of running the Met Office Unified Model (UM) with a grid spacing of 4 and 1

S. J. Cole; R. J. Moore; N. Roberts

2007-01-01

281

Data Assimilation at FLUXNET to Improve Models towards Ecological Forecasting (Invited)  

NASA Astrophysics Data System (ADS)

Dramatically increased volumes of data from observational and experimental networks such as FLUXNET call for transformation of ecological research to increase its emphasis on quantitative forecasting. Ecological forecasting will also meet the societal need to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-based models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today’s ecological models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting is data assimilation, which uses data to inform initial conditions and to help constrain a model during simulation to yield results that approximate reality as closely as possible. In an era with dramatically increased availability of data from observational and experimental networks, data assimilation is a key technique that helps convert the raw data into ecologically meaningful products so as to accelerate our understanding of ecological processes, test ecological theory, forecast changes in ecological services, and better serve the society. This talk will use examples to illustrate how data from FLUXNET have been assimilated with process-based models to improve estimates of model parameters and state variables; to quantify uncertainties in ecological forecasting arising from observations, models and their interactions; and to evaluate information contributions of data and model toward short- and long-term forecasting of ecosystem responses to global change.

Luo, Y.

2009-12-01

282

A neural network model to forecast Japanese demand for travel to Hong Kong  

Microsoft Academic Search

Apart from simple guesswork, time-series and regression techniques have largely dominated forecasting models for international tourism demand. This paper presents a new approach that uses a supervised feed-forward neural network model to forecast Japanese tourist arrivals in Hong Kong. The input layer of the neural network contains six nodes: Service Price, Average Hotel Rate, Foreign Exchange Rate, Population, Marketing Expenses,

Rob Law; Norman Au

1999-01-01

283

A dynamic artificial neural network model for forecasting time series events  

Microsoft Academic Search

Neural networks have shown to be an effective method for forecasting time series events. Traditional research in this area uses a network with a sequential iterative learning process based on the feed-forward, back-propagation approach. In this paper we present a dynamic neural network model for forecasting time series events that uses a different architecture than traditional models. To assess the

M. Ghiassi; H. Saidane; D. K. Zimbra

2005-01-01

284

Time series forecasting using a hybrid ARIMA and neural network model  

Microsoft Academic Search

Autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting during the past three decades. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional linear methods. ARIMA models and ANNs are often compared with mixed conclusions in terms of the superiority in

Guoqiang Peter Zhang

2003-01-01

285

Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices  

Microsoft Academic Search

While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime

Gordon W. Crawford; Michael C. Fratantoni

2003-01-01

286

MAFALDA: An early warning modeling tool to forecast volcanic ash dispersal and deposition  

Microsoft Academic Search

Forecasting the dispersal of ash from explosive volcanoes is a scientific challenge to modern volcanology. It also represents a fundamental step in mitigating the potential impact of volcanic ash on urban areas and transport routes near explosive volcanoes. To this end we developed a Web-based early warning modeling tool named MAFALDA (Modeling and Forecasting Ash Loading and Dispersal in the

S. Barsotti; L. Nannipieri; A. Neri

2008-01-01

287

Impact of Domain Size on Modeled Ozone Forecast for the Northeastern United States  

Microsoft Academic Search

This study investigates the impact of model domain extent and the specification of lateral boundary conditions on the forecast quality of air pollution constituents in a specific region of interest. A develop- mental version of the national Air Quality Forecast System (AQFS) has been used in this study. The AQFS is based on the NWS\\/NCEP Eta Model (recently renamed the

Pius Lee; Daiwen Kang; Jeff McQueen; Marina Tsidulko; Mary Hart; Geoff Dimego; Nelson Seaman; Paula Davidson

2008-01-01

288

THE EMERGENCE OF NUMERICAL AIR QUALITY FORECASTING MODELS AND THEIR APPLICATION  

EPA Science Inventory

In recent years the U.S. and other nations have begun programs for short-term local through regional air quality forecasting based upon numerical three-dimensional air quality grid models. These numerical air quality forecast (NAQF) models and systems have been developed and test...

289

Establishment of numerical beach-litter hindcast/forecast models: an application to Goto Islands, Japan.  

PubMed

This study attempts to establish a system for hindcasting/forecasting the quantity of litter reaching a beach using an ocean circulation model, a two-way particle tracking model (PTM) to find litter sources, and an inverse method to compute litter outflows at each source. Twelve actual beach survey results, and satellite and forecasted wind data were also used. The quantity of beach litter was hindcasted/forecasted using a forward in-time PTM with the surface currents computed in the ocean circulation model driven by satellite-derived/forecasted wind data. Outflows obtained using the inverse method was given for each source in the model. The time series of the hindcasted/forecasted quantity of beach litter were found consistent with the quantity of beach litter determined from sequential webcam images of the actual beach. The accuracy of the model, however, is reduced drastically by intense winds such as typhoons which disturb drifting litter motion. PMID:21093000

Kako, Shin'ichiro; Isobe, Atsuhiko; Magome, Shinya; Hinata, Hirofumi; Seino, Satoquo; Kojima, Azusa

2010-11-18

290

Forecasting gaming revenues in Clark County, Nevada: Issues and methods  

SciTech Connect

This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.

Edwards, B.K.; Bando, A.

1992-07-01

291

Forecasting gaming revenues in Clark County, Nevada: Issues and methods  

SciTech Connect

This paper describes the Western Area Gaming and Economic Response Simulator (WAGERS), a forecasting model that emphasizes the role of the gaming industry in Clark County, Nevada. Is is designed to generate forecasts of gaming revenues in Clark County, whose regional economy is dominated by the gaming industry. The model is meant to forecast Clark County gaming revenues and identifies the exogenous variables that affect gaming revenues. It will provide baseline forecasts of Clark County gaming revenues in order to assess changes in gaming-related economic activity resulting from changes in regional economic activity and tourism.

Edwards, B.K.; Bando, A.

1992-01-01

292

Forecasting and Futurology  

Microsoft Academic Search

The Institute of Applied Economic and Social Research has published forecasts of the Australian economy since the late 1960s. These forecasts (usually 12 to 18 months ahead) have been dominated by short-term macroeconomic factors. Compared with when the IAESR commenced its forecasting, there are now many forecasters who concentrate on the performance of the Australian economy over the short term.

David Johnson; Peter Kenyon

1993-01-01

293

Short period forecasting of catchment-scale precipitation. Part II: a water-balance storm model for short-term rainfall and flood forecasting  

NASA Astrophysics Data System (ADS)

A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid) within the higher resolution model (5 km grid). A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i) a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii) advected, steady-state predictions from the dynamic model, using "inferred updraughts", provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture) model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment in Somerset.

Bell, V. A.; Moore, R. J.

294

Engineering/economic end-use energy models  

SciTech Connect

The objective of this paper is to provide a glimpse of the current technology of engineering/economic end-use energy models. In providing this glimpse, it was found desirable to articulate three subsidiary objectives. The first objective describes end-use modeling within the broader context of an analytical framework capable of producing statistically sound and valid forecasts. The second objective highlights those aspects of the end-use modeling problem that are associated with technology and technology characterization. The third objective describes results of a policy application. The authors hope that the latter two objectives provide insights to the physics community concerning how and how well their inputs to the end-use modeling problem are employed. 19 references, 3 figures, 2 tables.

Hamblin, D.M.; Vineyard, T.A.

1985-01-01

295

Identification of fuzzy model for short-term load forecasting using evolutionary programming and orthogonal least squares  

Microsoft Academic Search

This paper presents a weather sensitive short-term load forecasting (STLF) algorithm, based on a novel fuzzy modeling strategy using evolutionary programming (EP) and orthogonal least squares (OLS). Traditional forecasting models based short-term load forecasting techniques have limitations especially when weather changes are seasonal. The proposed fuzzy modeling strategy mainly contributes to predicting the hourly load when the load change is

B. Ye; N. N. Yan; C. X. Guo; Y. J. Cao

2006-01-01

296

Using climate model ensemble forecasts for seasonal hydrologic prediction  

Microsoft Academic Search

Seasonal hydrologic forecasting has long played an invaluable role in the development and use of water resources. Despite notable advances in the science and practice of climate prediction, current approaches of hydrologists and water managers largely fail to incorporate seasonal climate forecast information that has become operationally available during the last decade. This study is motivated by the view that

Andrew Whitaker Wood

2003-01-01

297

Neural networks in forecasting models: Nile River application  

Microsoft Academic Search

The neural network approach is applied to the prediction of the flow of the River Nile. A multilayer feedforward network is constructed and trained by the backpropagation algorithm. We propose several different methods for single-step ahead forecast and multi-step ahead forecast in an attempt to get the least prediction error. These methods investigate different ways to preprocess the inputs and

S. El Shoura; M. El Sherif; A. Atiya; S. Shaheen

1998-01-01

298

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models  

NASA Astrophysics Data System (ADS)

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

2010-09-01

299

Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm  

Microsoft Academic Search

As far as the impact of tropospheric ozone (O3) on human heath and plant life are concerned, forecasting its daily maximum level is of great importance in Hong Kong as well as other metropolises in the world. This paper proposed a multi-layer perceptron (MLP) model with a novel hybrid training method to perform the forecasting task. The training method synergistically

Dong Wang; Wei-Zhen Lu

2006-01-01

300

Battlescale Forecast Model (BFM) Target Area Wind Speed Validation Over WSMR, NM Initial Results.  

National Technical Information Service (NTIS)

The Battlescale Forecast Model (BFM) was run on a 200 MHz Pentium PC using initialization and verification data collected during November and December 1974 at WSMR, NM. BFM target area artillery wind speed forecasts were verified at two locations on the n...

D. I. Knapp

1998-01-01

301

Toward Improving Water Supply Forecasts on the Carson River with a Physically Based Hydrologic Model  

Microsoft Academic Search

Researchers at the Desert Research Institute and the USBR are conducting research aimed at improving water supply forecasts on the Carson River as part of the Water 2025 initiative. The primary goal of the effort is to improve short, seasonal, and long term streamflow forecasts through the use of a physically based hydrologic model (MMS-PRMS) coupled with an operational river

S. Rajagopal; D. P. Boyle; G. Lamorey; S. Bassett; S. Coors; M. Mann

2005-01-01

302

Probabilistic forecasting at ungauged basins: using neighbour catchments for model calibration and updating  

Microsoft Academic Search

This study evaluates the quality of probabilistic streamflow forecasts at ungauged basins when using neighbour catchments for model calibration and updating. Many studies have been carried out to estimate the local parameters at ungauged basins for flow simulation and flow quantile estimation. However, approaches devoted to the question of streamflow forecasting at ungauged basins are rarer. In fact, in flow

Annie Randrianasolo; Maria-Helena Ramos; Vazken Andréassian

2010-01-01

303

Climate informed monthly streamflow forecasts for the Brazilian hydropower network using a periodic ridge regression model  

Microsoft Academic Search

summary Streamflow simulation and forecasts have been widely used in water resources management, particularly for flood and drought analysis and for the determination of optimal operational rules for reservoir sys- tems used for water supply and energy production. Here we include climate information in a periodic- auto-regressive model in order to provide monthly streamflow forecasts for 54 hydropower sites in

Carlos H. R. Lima; Upmanu Lall

2010-01-01

304

Threat Level Forecast for Ship's Oil Spill - Based on BP Neural Network Model  

Microsoft Academic Search

It's very important to assess the threat level in time when the ship's oil spill occurred, because the threat level forecast will help to come to a decision when dealing with the accident. BP neural network model is proposed in this paper to build a thread level forecast method for ship's oil spill accident. Train the BP neural network first

Cai Wenxue; Zheng Yanwu; Shi Yongqiang; Zhong Huiling

2009-01-01

305

Forecasting of Chaotic Cloud Absorption Time Series for Meteorological and Plume Dispersion Modeling  

Microsoft Academic Search

A nonlinear forecasting method based on the reconstruction of a chaotic strange attractor from about 1.5 years of cloud absorption data obtained from half-hourly Meteosat infrared images was used to predict the behavior of the time series 24 h in advance. The forecast values are then used by a meteorological model for daily prediction of plume transport from the As

V. P EREZ-MUNUZURI

306

The Principles and Practice of Time Series Forecasting and Business Modelling Using Neural Nets  

Microsoft Academic Search

This paper is intended as a ‘hands-on’ practical discussion of how and why neural networks are used in forecasting and business modelling. The need for forecasting is briefly examined. The theory of the multilayer perceptron neural network is then covered both qualitatively and in mathematical detail, including the methods of back-propagation of error and independent validation. The advantages of the

Richard G. Hoptroff

1993-01-01

307

An Upper Ocean Model for Operational Forecasts During MaudNESS  

Microsoft Academic Search

The MaudNESS experiment required onboard assimilation of weather data and forecasts, along with remote sensing of ice concentration, into real-time models in order to (i) determine most likely regions for encountering marginal upper ocean stability; (ii) forecast ice trajectories during passive drifts; and (iii) aid in determining optimum ship orientation to minimize \\

M. G. McPhee

2006-01-01

308

A New Model to Short-Term Power Load Forecasting Combining Chaotic Time Series and SVM  

Microsoft Academic Search

Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on Lyapunov exponents was established. The time

Dongxiao Niu; Yongli Wang

2009-01-01

309

Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression  

Microsoft Academic Search

As wind energy penetration continues to grow, there is a critical need for probabilistic forecasts of wind resources. In addition, there are many other societally relevant uses for forecasts of wind speed, ranging from aviation to ship routing and recreational boating. Over the past two decades, ensembles of dynamical weather prediction models have been developed, in which multiple estimates of

Thordis L. Thorarinsdottir; Tilmann Gneiting

2010-01-01

310

A comparative study of linear and nonlinear models for aggregate retail sales forecasting  

Microsoft Academic Search

The purpose of this paper is to compare the accuracy of various linear and nonlinear models for forecasting aggregate retail sales. Because of the strong seasonal fluctuations observed in the retail sales, several traditional seasonal forecasting methods such as the time series approach and the regression approach with seasonal dummy variables and trigonometric functions are employed. The nonlinear versions of

Ching-Wu Chu; Guoqiang Peter Zhang

2003-01-01

311

Northwest Energy Policy Project: energy demand modeling and forecasting final report  

Microsoft Academic Search

The Northwest Energy Policy Project was undertaken to develop the necessary tools for energy policy development in the Pacific Northwest states individually and as a region. Mathematical Sciences Northwest, Inc. (MSNW) prepared the demand forecasting model for this project. This volume is the final report and incorporates a discussion of alternative methods of demand forecasting, the detailed formulation of MSNW's

McHugh

1977-01-01

312

Comparison of Model Forecasts with Measured Effects of a Synthetic Oil in Pond Ecosystems.  

National Technical Information Service (NTIS)

Comparisons were made between the ecological effects of phenolic compounds measured in experimental ponds and the effects forecast by aquatic ecosystem models. The Standard WAter COlumn Model (SWACOM) was used to extrapolate acute toxicity data to estimat...

S. M. Bartell

1985-01-01

313

Modeling the wind-fields of accidental releases by mesoscale forecasting  

SciTech Connect

Modeling atmospheric releases even during fair weather can present a sever challenge to diagnostic, observed-data-driven, models. Such schemes are often handicapped by sparse input data from meteorological surface stations and soundings. Forecasting by persistence is only acceptable for a few hours and cannot predict important changes in the diurnal cycle or from synoptic evolution. Many accident scenarios are data-sparse in space and/or time. Here we describe the potential value of limited-area, mesoscale, forecast models for real-time emergency response. Simulated wind-fields will be passed to ARAC`s operational models to produce improved forecasts of dispersion following accidents.

Albritton, J.R.; Lee, R.L.; Mobley, R.L.; Pace, J.C. [Lawrence Livermore National Lab., CA (United States); Hodur, R.A.; Lion, C.S. [Navel Research Lab, Monterey, CA (United States)

1997-07-01

314

Application of Grey Model and Artificial Neural Networks to Flood Forecasting  

NASA Astrophysics Data System (ADS)

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall-runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km2 watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth-order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.

Kang, Moon Seong; Kang, Min Goo; Park, Seung Woo; Lee, Jeong Jae; Yoo, Kyung Hak

2006-04-01

315

Fuzzy computing based rainfall-runoff model for real time flood forecasting  

NASA Astrophysics Data System (ADS)

This paper analyses the skills of fuzzy computing based rainfall-runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time-steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one-step-ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one-step-ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times.

Nayak, P. C.; Sudheer, K. P.; Ramasastri, K. S.

2005-03-01

316

Application of an error statistics estimation method to the PSAS forecast error covariance model  

NASA Astrophysics Data System (ADS)

In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the parameters required by a, forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Physical-space Statistical Analysis System (PSAS) height-wind forecast error covariance model. This method consists of two components: the first component computes the error statistics by using the National Meteorological Center (NMC) method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula, to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation (MLE) with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office (GMAO), NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.

Yang, R. H.; Guo, J.; Riishojgaard, P.

2006-01-01

317

Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran  

NASA Astrophysics Data System (ADS)

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

Soltanzadeh, I.; Azadi, M.; Vakili, G. A.

2011-07-01

318

A multiple model assessment of seasonal climate forecast skill for applications  

NASA Astrophysics Data System (ADS)

Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere-land-ocean models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast for applications. Results suggest there is a deficiency of skill in the forecasts beyond month-1, with precipitation having a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also be seen as a benchmark of current climate prediction capability using (dynamic) couple models.

Lavers, David; Luo, Lifeng; Wood, Eric F.

2009-12-01

319

An Empirical Solar Wind Forecast Model From The Chromosphere  

NASA Astrophysics Data System (ADS)

Recently, we [McIntosh and Leamon, ApJL, 624, 117, 2005] correlated the inferred topography of the solar chromospheric plasma with in situ solar wind velocity and composition data measured at 1~AU. Specifically, the measured separation in height of the TRACE 1600Å\\ and 1700Å\\ UV band pass filters correlate very strongly with solar wind velocity and inversely with the ratio of ionic oxygen (O^{7+/O^{6+}}) densities. Here, we build on our previous results by presenting initial results of a model developed to so predict interplanetary solar wind conditions, using SOHO/MDI magnetograms with 96 minute cadence as proxies of chromospheric topography as input. Specifically, we use the observed correlation between the measured chromospheric travel-time and the magnetic field strength to allow us to convert the into a (reasonable) full-disk travel-time diagnostic (in place of limited field of view TRACE observations). Maps of full-disk travel-time are scaled to wind diagnostic maps which are then "forward" mapped into the heliosphere using a PFSS model. The resulting wind forecast matches the observed state of the solar wind remarkably well for a simple model.

Leamon, R. J.; McIntosh, S. W.

2006-12-01

320

Risk forecasting and evaluating model of environmental pollution accident.  

PubMed

Environmental risk (ER) factors come from ER source and they are controlled by the primary control mechanism (PCM) of environmental risk, due to the self failures or the effects of external environment risk trigger mechanism, the PCM could not work regularly any more, then, the ER factors will release environmental space, and an ER field is formed up. The forming of ER field does not mean that any environmental pollution accident (EPA) will break out; only the ER receptors are exposed in the ER field and damaged seriously, the potential ER really turns into an actual EPA. Researching on the general laws of evolving from environmental risk to EPA, this paper bring forwards a relevant concept model of risk forecasting and evaluating of EPA. This model provides some scientific methods for risk evaluation, prevention and emergency response of EPA. This model not only enriches and develops the theory system of environment safety and emergency response, but also acts as an instruction for public safety, enterprise' s safety management and emergency response of the accident. PMID:16295902

Zeng, Wei-hu; Cheng, Sheng-tong

2005-01-01

321

How Useful Are Regional Climate Models For Downscaling Seasonal Forecasts?  

NASA Astrophysics Data System (ADS)

A longstanding yet very important question concerns the additional value derived from labor intensive regional climate models (RCMs) nested within GCM seasonal forecast models, over and above simple statistical methods of downscaling. This paper compares the two types of downscaling of precipitation "hindcasts" over the data-rich region of the Philippines, using observed data from 77 raingauges for the April-June monsoon onset season. Spatial interpolation of RCM and GCM grid box values to station locations is compared with cross-validated regression-based techniques such as canonical correlation analysis. The GCM "hindcasts" are formed from an ensemble of simulations from the ECHAM4.5 model at T42 resolution made with observed SSTs prescribed, over the 1977-2004 period. The RegCM3 with 25km resolution is nested within each of a 10-member GCM ensemble over the Philippines. To first order, we find that anomaly correlation skill at the station scale for simulations of seasonal total rainfall and monsoon onset date is quite similar using all the techniques considered, including simple spatial interpolation of the GCM values. The RCM has significantly smaller RMS error than the "raw" interpolated GCM, although statistical correction can greatly improve the latter. We examine the role of the availability of sufficiently long records of observed data as a deciding factor, which enters as a means to validate both types of the hindcasts, while being needed in addition to train the more "data hungry" statistical downscaling methods.

Robertson, A. W.; Qian, J.; Moron, V.; Tippett, M.; Lucero, A.

2010-12-01

322

Use of a snowmelt model for weekly flood forecast for a major reservoir in Lithuania  

NASA Astrophysics Data System (ADS)

A snowmelt model is used for the weekly forecast of daily discharges in the Kaunas reservoir, Lithuania. The results are used to feed a risk-based decision-making model developed by the first author for dam operation during floods. Physically based calibration of a degree-day model is carried out and coupled with flow routing using Nash's instantaneous unit hydrograph theory. Temperature forecast is used as the driving variable. Due to the relative smoothness of snowmelt over time and the considerable basin size, the model provides acceptable results. Kalman filtering is then used to merge the estimates from the snowmelt model with those from an ARIMA flow model, resulting in better forecasting than that using each method alone. Uncertainty analysis of the snowmelt-model results is then carried out, showing considerable influence of the main parameter degree-day and of soil moisture conditions. Therefore these must be accurately estimated for forecasting purposes during flood events.

Simaityte, Jurgita; Bocchiola, Daniele; Augutis, Juozas; Rosso, Renzo

323

A heuristic method for forecasting chaotic time series based on economic variables  

Microsoft Academic Search

Time series is one of the most attractive and mysterious mathematical subjects. Weather temperature, rainfall, water flow volume of a river and other similar cases in meteorology are known and predictable time series; amount of load peak, electricity price and other similar cases in electrical engineering are considerable time series. Time series forecasting is highly taken into account in economy.

Reza Reyhani; Amir Masud Eftekhari Moghadam

2011-01-01

324

The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations  

NASA Astrophysics Data System (ADS)

Compared with traditional real-time forecasting, this paper proposes a Grey Markov Model (GMM) to forecast the maximum water levels at hydrological stations in the estuary area. The GMM combines the Grey System and Markov theory into a higher precision model. The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values, and thus gives forecast results involving two aspects of information. The procedure for forecasting annul maximum water levels with the GMM contains five main steps: 1) establish the GM (1, 1) model based on the data series; 2) estimate the trend values; 3) establish a Markov Model based on relative error series; 4) modify the relative errors caused in step 2, and then obtain the relative errors of the second order estimation; 5) compare the results with measured data and estimate the accuracy. The historical water level records (from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin, China are utilized to calibrate and verify the proposed model according to the above steps. Every 25 years' data are regarded as a hydro-sequence. Eight groups of simulated results show reasonable agreement between the predicted values and the measured data. The GMM is also applied to the 10 other hydrological stations in the same estuary. The forecast results for all of the hydrological stations are good or acceptable. The feasibility and effectiveness of this new forecasting model have been proved in this paper.

Dong, Sheng; Chi, Kun; Zhang, Qiyi; Zhang, Xiangdong

2012-03-01

325

Modeling and forecasting of KLCI weekly return using WT-ANN integrated model  

NASA Astrophysics Data System (ADS)

The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

2013-04-01

326

Commuter Airline Forecasts.  

National Technical Information Service (NTIS)

This publication presents forecasts of commuter air carrier activity and describes the models designed for forecasting Conterminous United States, Puerto Rico and the Virgin Islands, Hawaii, and individual airport activity. These forecasts take into accou...

H. Medville C. Starry G. Bernstein

1981-01-01

327

Forecast performance assessment of a kinematic and a magnetohydrodynamic solar wind model  

NASA Astrophysics Data System (ADS)

evaluation of operational models guides forecasters on use of their products, focuses model developers in making improvements, and informs other modelers considering use of the output for forcing data. Two operational solar wind models, the Enlil magnetohydrodynamic model, and the Wang-Sheeley-Arge kinematic model, were executed daily in 2007-2011 from solar photosphere magnetograms compiled from the Global Oscillation Network Group telescope system. The original (uncorrected) magnetic field specification and a zero point-corrected (ZPC) version were used as inner boundary conditions (IBCs) in separate 7 day forecast executions. Forecasts of solar wind radial speed (Vsw) and interplanetary magnetic field (IMF) polarity were compared with observations from the Advanced Composition Explorer satellite. Forecast verification metrics were computed by forecast day, year, and uncorrected or corrected IBCs. High speed events (HSEs) and IMF polarity changes (IPCs) predicted and observed were compared. Neither model exhibited a significant systematic error except in 2009, when both failed to represent the slow solar wind. Using the ZPC initial conditions resulted in smaller forecast-observation differences in the years with greater Vsw variance. This was due to in part to reduced variance in the Vsw predictions from the ZPC IBCs. Differences were nil or worse in the other years. The time-varying component of the forecast-observation differences was smallest at forecast days 3 to 5, followed by a sharp rise. Impact of ZPCs on IMF polarity predictions was small. HSE prediction performance depended on detection algorithm used. Both models under predicted the number of forecast periods having IPCs.

Norquist, Donald C.

2013-01-01

328

Modelling eWork in Europe: Estimates, Models and Forecasts from the EMERGENCE Project. IES Report.  

ERIC Educational Resources Information Center

|A study combined results of a survey of employers in 18 European countries to establish the extent to which they are currently using eWork with European official statistics to develop models, estimates, and forecasts of the numbers of eWorkers in Europe. These four types of "individual" eWork were identified: telehomeworking; multilocational…

Bates, P.; Huws, U.

329

Using High Resolution Numerical Weather Prediction Models to Reduce and Estimate Uncertainty in Flood Forecasting  

NASA Astrophysics Data System (ADS)

Forecast rainfall from Numerical Weather Prediction (NWP) and/or nowcasting systems is a major source of uncertainty for short-term flood forecasting. One approach for reducing and estimating this uncertainty is to use high resolution NWP models that should provide better rainfall predictions. The potential benefit of running the Met Office Unified Model (UM) with a grid spacing of 4 and 1 km compared to the current operational resolution of 12 km is assessed using the January 2005 Carlisle flood in northwest England. These NWP rainfall forecasts, and forecasts from the Nimrod nowcasting system, were fed into the lumped Probability Distributed Model (PDM) and the distributed Grid-to-Grid model to predict river flow at the outlets of two catchments important for flood warning. The results show the benefit of increased resolution in the UM, the benefit of coupling the high- resolution rainfall forecasts to hydrological models and the improvement in timeliness of flood warning that might have been possible. Ongoing work aims to employ these NWP rainfall forecasts in ensemble form as part of a procedure for estimating the uncertainty of flood forecasts.

Cole, S. J.; Moore, R. J.; Roberts, N.

2007-12-01

330

Relativistic electron flux forecast at geostationary orbit using Kalman filter based on multivariate autoregressive model  

NASA Astrophysics Data System (ADS)

The relativistic electron population at MeV energy in the Van Allen radiation belts at geostationary orbit largely varies in association with solar wind disturbances. To provide alerts of possible satellite malfunctions due to deep-dielectric charging during relativistic electron enhancements, the National Institute of Information and Communications Technology, Japan, developed an algorithm to forecast daily >2 MeV electron flux variations at geostationary orbit using a multivariate autoregressive model. We examined model accuracy by using solar wind speed, north-south component of the magnetic field, and dynamic pressure by inputting them as explanatory variates. The results showed that a combination of all three variates was most effective in reducing the prediction error. We focus here on the four-variate autoregressive model and handle it using the Kalman filter. The time evolution of the forecast is given by the conditional normal distribution: the peak value of forecast probability and the error range. The error range estimation is useful for users who utilize forecasts for operation of the satellites. We investigated the prediction efficiency of +1 day forecasts by evaluating forecast and observation data for a whole solar cycle (1999-2008) every 2 years. The prediction efficiency maintained at more than 69% throughout the solar cycle, although it depends on the phase of the cycle. Comparisons of the prediction efficiencies revealed that our model exhibited the best performance of conventional forecast models, particularly in solar active periods.

Sakaguchi, K.; Miyoshi, Y.; Saito, S.; Nagatsuma, T.; Seki, K.; Murata, K. T.

2013-02-01

331

Probabilistic streamflow predictions combining ensemble meteorological forecasts and a multi-model approach  

NASA Astrophysics Data System (ADS)

Probabilistic streamflow prediction based on past climate records or meteorological forecasts have drawn much attention in recent years. It is usually incorporated into operational forecasting systems by government agencies and industries to deal with water resources management and regulation problems. This work presents an operational prototype for short to medium term ensemble streamflow predictions over Quebec, Canada. The system uses ensemble meteorological forecasts for short term (up to 7 days) forecasting, transitioning to a stochastic weather generator conditioned on historical data for the period exceeding 7 days. The precipitation and temperature series are then fed into a combination of 32 hydrology models to account for both the meteorological and hydrology modelling uncertainties. A novel post-processing approach was implemented to correct the biases and the under-dispersion of ensemble meteorological forecasts. This post-processing approach links the mean of the ensemble meteorological forecast to parameters of a stochastic weather generator (absolute probability of precipitation and observed precipitation mean in the case of precipitation). The stochastic weather generator is then used to generated unbiased times series with accurate spread. Results show that the post-processed meteorological forecasts displayed skill for a period up to 7 days for both precipitation and temperature. The ensemble streamflow prediction displayed more skill than when using the deterministic forecast or the stochastic weather generator not conditioned on the ensemble meteorological forecasts. To tackle the uncertainty linked to the hydrology model, 4 different models calibrated with up to 9 different efficiency metrics (for a combination of 32 models/calibrations). Nine different averaging schemes were compared to attribute weights to the 32 combinations. The best averaging method (Granger-Ramanathan) produced estimates with a much better efficiency than the best performing model, while removing all biases linked to the hydrology modelling.

Chen, Jie; Brissette, François; Arsenault, Richard; Gatien, Philippe; Roy, Pierre-Olivier; Li, Zhi; Turcotte, Richard

2013-04-01

332

Residential Saudi load forecasting using analytical model and Artificial Neural Networks  

NASA Astrophysics Data System (ADS)

In recent years, load forecasting has become one of the main fields of study and research. Short Term Load Forecasting (STLF) is an important part of electrical power system operation and planning. This work investigates the applicability of different approaches; Artificial Neural Networks (ANNs) and hybrid analytical models to forecast residential load in Kingdom of Saudi Arabia (KSA). These two techniques are based on model human modes behavior formulation. These human modes represent social, religious, official occasions and environmental parameters impact. The analysis is carried out on residential areas for three regions in two countries exposed to distinct people activities and weather conditions. The collected data are for Al-Khubar and Yanbu industrial city in KSA, in addition to Seattle, USA to show the validity of the proposed models applied on residential load. For each region, two models are proposed. First model is next hour load forecasting while second model is next day load forecasting. Both models are analyzed using the two techniques. The obtained results for ANN next hour models yield very accurate results for all areas while relatively reasonable results are achieved when using hybrid analytical model. For next day load forecasting, the two approaches yield satisfactory results. Comparative studies were conducted to prove the effectiveness of the models proposed.

Al-Harbi, Ahmad Abdulaziz

333

Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting  

Microsoft Academic Search

River stage forecasting is an important issue in water resources management and real-time prediction of extreme floods. The\\u000a present study investigates the performance of the wavelet regression (WR) technique in daily river stage forecasting. The\\u000a WR model was improved combining two methods, discrete wavelet transform and a linear regression model. Two different WR models\\u000a were developed using the stage sub-time

Ozgur Kisi

2011-01-01

334

One-Way Coupled Atmospheric-Lake Model Forecasts for Lake Erie  

Microsoft Academic Search

A one-way coupled atmospheric-lake modeling system was developed to generate short-term, mesoscale lake circulation, water level and temperature forecasts for Lake Erie. The coupled system consisted of the semi-operational version of the Penn State University\\/National Center for Atmospheric Research three-dimensional, mesoscale meteorological model (MM4) and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS). The coupled system

John Gormley Walsh Kelley

1995-01-01

335

Small, Simple and Fine: The OeNB Forecast Model for CESEE 1  

Microsoft Academic Search

This short paper describes the new forecasting tool used by the OeNB to derive near-term forecasts on GDP and imports for five CESEE countries (Bulgaria, Croatia, Czech Republic, Hungary, and Poland). An error correction (EC) model is estimated separately for each country by means of seemingly unrelated regressions. Each country-specific macro model consists of 6 structural cointegration relationships modelling private

Jesus Crespo-Cuaresma; Martin Feldkircher; Tomas Slacik; Julia Woerz

336

Forecasting Dengue Haemorrhagic Fever Cases in Southern Thailand using ARIMA Models  

Microsoft Academic Search

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

S. Promprou; M. Jaroensutasinee; K. Jaroensutasinee

2006-01-01

337

Generation of Three-Dimensional Lake Model Forecasts for Lake Erie  

Microsoft Academic Search

A one-way coupled atmospheric-lake modeling system was developed to generate short-term, mesoscale lake circulation, water level, and temperature forecasts for Lake Erie. The coupled system consisted of the semi- operational versions of the Pennsylvania State University-National Center for Atmospheric Research three- dimensional, mesoscale meteorological model (MM4), and the three-dimensional lake circulation model of the Great Lakes Forecasting System (GLFS). The

John G. W. Kelley; Jay S. Hobgood; Keith W. Bedford; David J. Schwab

1998-01-01

338

USING BOX-JENKINS MODELS TO FORECAST FISHERY DYNAMICS: IDENTIFICATION, ESTIMATION, AND CHECKING  

Microsoft Academic Search

Box·Jenkins models are suggested as appropriate models for forecasting fishery dynamics. Unlike standard production models, these models are empirical, dynamic, stochastic models. Box·Jenkins models are not biased when estimating relationships between catch and effort, as are standard production models. The use of these techniques is illustrated on catch and effort data for the skipjack tuna fleet in Hawaii. An actual

Roy MENDELSSOHN

1981-01-01

339

Modelling and forecasting monthly and daily river discharge data using hybrid models and considering autoregressive heteroscedasticity  

NASA Astrophysics Data System (ADS)

Hybrid modelling, used for simulation and forecasting of hydrological time series, involving both process-based and data-driven types of models combines the available domain knowledge and process physics with the recent advances in data driven tools. In this way, complex hydrological processes can be modelled and forecasted by decomposing the problem into several smaller sub - problems and using process physics based models where these are most appropriate, and data dictated tools (such as ANN, time series models or traditional statistics) for the residual data, when necessary. The fitting and forecasting performance of such models have to be explored case based. So far, only a few attempts to apply various nonlinear time series models within such a framework were reported in the hydrological modelling literature. This contribution presents results concerning the possibility to use GARCH type of models for such purposes. More specifically, error time series from two hydrological conceptual models were analyzed (applied on time series measured from the Hron and Morava Rivers in Slovakia), concentrating on the improvement of the modelling and forecasting performance of these models. The goal of investigation was to try to expand the knowledge in the time series modelling of hydrological model error time series with the aim to test and develop appropriate methods for various time steps from the GARCH family of models. In order to achieve this, following steps were taken: 1. The presence of heteroscedasticity was verified in time series. 2. A model from the GARCH family was fitted on the data, comparing the fit with a fit of an ARMA model. 3. One - step - ahead forecasts from the fitted models were produced, performing comparisons. The investigation of model properties and performances was thoroughly tested under various conditions of their future practical applications. In general, heteroscedasticity was present in the majority of the error time series of the hydrological models. However, the GARCH family of models proved to be suited in removing it only in daily time step. The basic GARCH model was not applicable on any of the time series. In all other investigated cases, the EGARCH(1,1) model had to be used. Unlike in econometric time series, where the so called leverage effect (i.e. the series reacts more strongly to negative changes) is present and pointed out by this model, here the data tends to react more strongly on positive changes. In this particular case it was found, that the general property of hydrological processes, that the rise of discharge is rainfall driven (a highly nonlinear chaotic intermittent process) and the decrease of discharge is ruled by the damping effects of the water storage in the driven system (catchment or river reach), is present also in the hydrological model error series. This shows, that the modelling and forecasting of floods (pulse like rising discharge) is a more demanding task than that of droughts (slowly decreasing flows). Even though the GARCH models did show partial improvements in the modelling and forecasting of flows, they still have several serious disadvantages (such as high sensitivity to the chosen fitting period) and possible further use should be further investigated. These results are of importance with respect to future attempts of modelling of error time series of hydrological models in such hybrid frameworks. They underpin the need of a non-mechanistic approach in the case based analysis of such data and the physical interpretation of statistical modelling results.

Szolgayova, Elena

2010-05-01

340

Data-driven models to forecast PM10 concentration  

Microsoft Academic Search

The research activity described in this paper concerns the study of the phenomena responsible for the urban and suburban air pollution. The analysis carries on the work already developed by the NeMeFo (neural meteo forecasting) research project for meteorological data short-term forecasting. The study analyzed the air pollution principal causes and identified the best subset of features (meteorological data and

Giovanni Raimondo; Alfonso Montuori; Walter Moniaci; Eros Pasero; Esben Almkvist

2007-01-01

341

REAP Economic Demographic Model: Technical Description.  

National Technical Information Service (NTIS)

The report describes the structure and data base of a computerized model for projecting localized economic, demographic, and fiscal impacts of new energy developments. The model provides baseline and single or multiple-project impact projections for a 15-...

T. Hertsgaard S. Murdock N. Toman M. Henry R. Ludtke

1978-01-01

342

A stochastic space-time rainfall forecasting system for real time flow forecasting II: Application of SHETRAN and ARNO rainfall runoff models to the Brue catchment  

NASA Astrophysics Data System (ADS)

Key issues involved in converting MTB ensemble forecasts of rainfall into ensemble forecasts of runoff are addressed. The physically-based distributed modelling system, SHETRAN, is parameterised for the Brue catchment, and used to assess the impact of averaging spatially variable MTB rainfall inputs on the accuracy of simulated runoff response. Averaging is found to have little impact for wet antecedent conditions and to lead to some underestimation of peak discharge under dry catchment conditions. The simpler ARNO modelling system is also parameterised for the Brue and SHETRAN and ARNO calibration and validation results are found to be similar. Ensemble forecasts of runoff generated using both SHETRAN and the simpler ARNO modelling system are compared. The ensemble is more spread out with the SHETRAN model, and a likely explanation is that the ARNO model introduces too much smoothing. Nevertheless, the forecasting performance of the simpler model could be adequate for flood warning purposes.

Mellor, D.; Sheffield, J.; O'Connell, P. E.; Metcalfe, A. V.

343

Appraisal of Forecast Value for Groundwater Resources Management  

NASA Astrophysics Data System (ADS)

Seasonal climate forecasts present an opportunity to increase the efficiency with which water resources are managed. However, the probabilistic nature of forecasts poses challenges to potential users and complicates evaluation of the forecasts' quality and value. In this study we use Bayesian decision modeling to evaluate the performance of a seasonal precipitation forecast. We generate an optimal decision map by which probabilistic categorical forecasts are re-categorized and evaluated. In this way, forecast performance is assessed not in terms of the observed climate state but rather in terms of the decision indicated by the forecast. Preposterior analysis via stochastic dynamic programming is used to determine the expected value of the forecast. The application setting is the Palar River basin in Tamil Nadu, India, where demand for water exceeds economically available resources leading to income loss, economic displacement and environmental degradation. Instead of targeting forecasts for use by farmers, we propose that water managers use forecasts to set economic parameters to signal the expected availability of water in the coming season. The economic signal promotes efficient use of water while mitigating the farmers' personal risk of forecast-based decisions.

Brown, C.; Rogers, P.

2004-05-01

344

Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts  

NASA Astrophysics Data System (ADS)

A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

Mittermaier, M. P.

2008-05-01

345

Forecasting aggregate retail sales  

Microsoft Academic Search

Like many other economic time series, US aggregate retail sales have strong trend and seasonal patterns. How to best model and forecast these patterns has been a long-standing issue in time-series analysis. This article compares artificial neural networks and traditional methods including Winters exponential smoothing, Box–Jenkins ARIMA model, and multivariate regression. The results indicate that on average ANNs fare favorably

Ilan Alon; Min Qi; Robert J. Sadowski

2001-01-01

346

A simple model for forecast of coastal algal blooms  

NASA Astrophysics Data System (ADS)

In eutrophic sub-tropical coastal waters around Hong Kong and South China, algal blooms (more often called red tides) due to the rapid growth of microscopic phytoplankton are often observed. Under favourable environmental conditions, these blooms can occur and subside over rather short time scales—in the order of days to a few weeks. Very often, these blooms are observed in weakly flushed coastal waters under calm wind conditions—with or without stratification. Based on high-frequency field observations of harmful algal blooms at two coastal mariculture zones in Hong Kong, a mathematical model has been developed to forecast algal blooms. The model accounts for algal growth, decay, settling and vertical turbulent mixing, and adopts the same assumptions as the classical Riley, Stommel and Bumpus model (Riley, G.A., Stommel, H., Bumpus, D.F., 1949. Quantitative ecology of the plankton of the western North Atlantic. Bulletin of the Bingham Oceanographic Collection Yale University 12, 1 169). It is shown that for algal blooms to occur, a vertical stability criterion, E < 4?l2/?2, must be satisfied, where E, ?, l are the vertical turbulent diffusivity, algal growth rate, and euphotic layer depth respectively. In addition, a minimum nutrient threshold concentration must be reached. Moreover, with a nutrient competition consideration, the type of bloom (caused by motile or non-motile species) can be classified. The model requires as input simple and readily available field measurements of water column transparency and nutrient concentration, and representative maximum algal growth rate of the motile and non-motile species. In addition, with the use of three-dimensional hydrodynamic circulation models, simple relations are derived to estimate the vertical mixing coefficient as a function of tidal range, wind speed, and density stratification. The model gives a quick assessment of the likelihood of algal bloom occurrence, and has been validated against field observations over a 4-year period. The model helps to explain the observed spatial and temporal patterns of bloom occurrences in relation to the vertical turbulence and nutrient condition. The success of the model points the way to the development of real time management models for disaster mitigation.

Wong, Ken T. M.; Lee, Joseph H. W.; Hodgkiss, I. J.

2007-08-01

347

Coupling global chemistry transport models to ECMWF's integrated forecast system  

NASA Astrophysics Data System (ADS)

The implementation and application of a newly developed coupled system combining ECMWF's integrated forecast system (IFS) with global chemical transport models (CTMs) is presented. The main objective of the coupled system is to enable the IFS to simulate key chemical species without the necessity to invert the complex source and sink processes such as chemical reactions, emission and deposition. Thus satellite observations of atmospheric composition can be assimilated into the IFS using its 4D-VAR algorithm. In the coupled system, the IFS simulates only the transport of chemical species. The coupled CTM provides to the IFS the concentration tendencies due to emission injection, deposition and chemical conversion. The CTMs maintain their own transport schemes and are fed with meteorological data at hourly resolution from the IFS. The CTM used in the coupled system can be either MOZART-3, TM5 or MOCAGE. The coupling is achieved via the special-purpose OASIS4 software. The scientific integrity of the coupled system is proven by analysing the difference between stand-alone CTM simulations and the tracer fields in the coupled IFS. The IFS concentration fields match the CTM fields for about 48 h with the biggest differences occurring in the planetary boundary layer (PBL). The coupled system is a good test bed for process-oriented comparison of the coupled CTM. As an example, the vertical structure of chemical conversion and emission injection is studied for a ten day period over Central Europe for the three CTMs.

Flemming, J.; Inness, A.; Flentje, H.; Huijnen, V.; Moinat, P.; Schultz, M. G.; Stein, O.

2009-07-01

348

Coupling global chemistry transport models to ECMWF's integrated forecast system  

NASA Astrophysics Data System (ADS)

The implementation and application of a newly developed coupled system combining ECMWF's integrated forecast system (IFS) with global chemical transport models (CTMs) is presented. The main objective of the coupled system is to enable the IFS to simulate key chemical species without the necessity to invert the complex source and sink processes such as chemical reactions, emission and deposition. Thus satellite observations of atmospheric composition can be assimilated into the IFS using its 4D-VAR algorithm. In the coupled system, the IFS simulates only the transport of chemical species. The coupled CTM provides to the IFS the concentration tendencies due to emission injection, deposition and chemical conversion. The CTMs maintain their own transport schemes and are fed with meteorological data at hourly resolution from the IFS. The CTM used in the coupled system can be either MOZART-3, TM5 or MOCAGE. The coupling is achieved via the special-purpose software OASIS4. The scientific integrity of the coupled system is proven by analysing the difference between stand-alone CTM simulations and the tracer fields in the coupled IFS. The IFS concentration fields match the CTM fields for about 48 h with the biggest differences occurring in the planetary boundary layer (PBL). The coupled system is a good test bed for process-oriented comparison of the coupled CTM. As an example, the vertical structure of chemical conversion and emission injection is studied for a ten day period over Central Europe for the three CTMs.

Flemming, J.; Inness, A.; Flentje, H.; Huijnen, V.; Moinat, P.; Schultz, M. G.; Stein, O.

2009-12-01

349

Multi-model ensemble forecasting and glider path planning in the Mid-Atlantic Bight  

NASA Astrophysics Data System (ADS)

During the first two weeks of November 2009, a field experiment was conducted in the Mid-Atlantic Bight region to demonstrate a coastal ocean observatory that can collect observations from heterogeneous platforms and forecast fields from four different ocean models, provide multi-model ensemble forecasts based on either an equal weighting (EQ) or objective weighting (OBJ) method, and use model forecasts in a path planning system to relocate autonomous gliders. This experiment is a prototype for the command and control component of cyberinfrastructure of the Ocean Observatories Initiative funded by the National Science Foundation. The four individual models use different forcing fields, boundary conditions and data assimilation techniques, and have resolutions varying from 2km to 15km. Our results indicate that for sea surface temperature and surface currents, the OBJ ensemble outperforms the four individual models, while the EQ ensemble can also provide an effective way to improve individual model forecasts. In terms of glider path planning, the OBJ ensemble has a performance similar to the best individual model, which has the finest horizontal resolution. This field experiment demonstrates the first-ever use of ensemble current forecasts to guide glider path planning in the context of real-time data collection and ocean model forecasting.

Wang, Xiaochun; Chao, Yi; Thompson, David R.; Chien, Steve A.; Farrara, John; Li, Peggy; Vu, Quoc; Zhang, Hongchun; Levin, Julia C.; Gangopadhyay, Avijit

2013-07-01

350

Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling  

Microsoft Academic Search

When choosing the rainfall-runoff modeling approach to be integrated in a river flow forecasting system, two crucial issues are the minimum data requirement for calibration purposes and the reliability of the predictions over different time horizons (lead-times). The paper presents an investigation of the real-time forecasting ability of a conceptual and a neural network model, comparing the performances obtainable for

Elena Toth; Armando Brath

2007-01-01

351

Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models  

Microsoft Academic Search

We extend Diebold and Li’s dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and

Wei-Choun Yu; Eric Zivot

2011-01-01

352

Forecasting the Term Structures of Treasury and Corporate Yields: Dynamic Nelson-Siegel Models Evaluation  

Microsoft Academic Search

Abstract We extend Diebold and Li’s dynamic,Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of state-space approach, and using nine different ratings for corporate bonds. We find that the dynamic,Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds in the short-term forecast horizon and on the high-yield bonds

Wei-choun Yu; Eric Zivot

353

Automatic Arima Time Series Modeling And Forecasting Adaptive Input\\/Output Prefetching  

Microsoft Academic Search

This thesis presents a comprehensive software framework - Automodeler - to provide automaticmodeling and forecasting for input\\/output (I\\/O) request interarrival times. In Automodeler,ARIMA models of interarrival times are automatically identified and built during application ex-ecution. Model parameters are recursively estimated in real-time for every new request arrival,adapting to changes that are intrinsic or external to the running application. Online forecasts

Nancy Ngoc Tran

2002-01-01

354

Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models  

Microsoft Academic Search

In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR\\/ARX (''X'' stands for exogenous\\/fundamental variable -– system load in our study), AR\\/ARX-GARCH, TAR\\/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-of-sample point and interval forecasting in

Adam Misiorek; Stefan Trueck; Rafal Weron

2006-01-01

355

Applying Forecast Models from the Center for Integrated Space Weather Modeling  

NASA Astrophysics Data System (ADS)

The Center for Integrated Space Weather Modeling (CISM) has developed three forecast models (FMs) for the Sun-Earth chain. They have been matured by various degrees toward the operational stage. The Sun-Earth FM suite comprises empirical and physical models: the Planetary Equivalent Amplitude (AP-FM), the Solar Wind (SW- FM), and the Geospace (GS-FM) models. We give a brief overview of these forecast models and touch briefly on the associated validation studies. We demonstrate the utility of the models: AP-FM supporting the operations of the AIM (Aeronomy of Ice in the Mesosphere) mission soon after launch; SW-FM providing assistance with the interpretation of the STEREO beacon data; and GS-FM combining model and observed data to characterize the aurora borealis. We will then discuss space weather tools in a more general sense, point out where the current capabilities and shortcomings are, and conclude with a look forward to what areas need improvement to facilitate better real-time forecasts.

Gehmeyr, M.; Baker, D. N.; Millward, G.; Odstrcil, D.

2007-12-01

356

Microcomputers and Dynamic Economic Models.  

ERIC Educational Resources Information Center

|Two computer programs that simulate the multiplier and accelerator processes are described. The simulations can be used in college-level economics classes or by pupils using their own personal computers at home. Very little knowledge of programing is required to implement the simulations. (Author/RM)|

Taylor, Peter

1985-01-01

357

Simultaneity in Economics Learning Models.  

ERIC Educational Resources Information Center

|An explanation of the statistical problems encountered in economics education research with using ordinary least squares (OLS) rather than two-stage least squares (TSLS) in the context of simultaneous structural equations is provided. When endogeneous variables are simultaneously related in a system of equations, alternative statistical…

Walstad, William B.

358

QUANTITATIVE PRECIPITATION FORECASTING FOR A SMALL URBAN AREA: USE OF A HIGH-RESOLUTION NUMERICAL WEATHER PREDICTION MODEL  

Microsoft Academic Search

Different sources of uncertainty affect the rainfall forecasting by Numerical Weather Prediction (NWP) models, as the atmosphere is a chaotic non-linear system highly sensitive to small changes in the initial conditions. Thus, it is now be- coming an operational practice to produce probabilistic forecasts, which make use of ensemble systems and are based upon the notion that deterministic forecasts are

M. A. Rico-Ramirez; A. N. A. Schellart; S. Liguori; A. J. Saul

359

High resolution operational air quality forecast for Poland and Central Europe with the GEM-AQ model - EcoForecast System  

NASA Astrophysics Data System (ADS)

The air quality forecast is an important component of the environmental assessment system. The "Clean Air for Europe" (CAFE) Directive 2008/50/EC stipulates a need for numerical modelling in order to support public information services to interpret measurements of pollutants concentrations and to prepare and evaluate air quality plans. Most European countries have developed model-based air quality modelling and information services. We will present the design strategy, development and implementation of a regional high resolution forecasting system that was implemented in Poland. The new national high resolution air quality forecasting system has evolved from a semi-operational chemical weather system EcoForecast.EU which is based on the GEM-AQ model (Kaminski et al., 2008). GEM-AQ is a comprehensive chemical weather model where air quality processes (chemistry and aerosols), troposphere and stratospheric chemistry are implemented on-line in the operational weather prediction model, the Global Environmental Multiscale (GEM) model (Cote et al, 1998), developed at Environment Canada. For these applications, the model is run on a global variable resolution grid with horizontal spacing of 15 km over Europe. In the vertical there are 28 hybrid levels, with the top at 10 hPa. A high resolution nested forecast at 5 km resolution over Poland (and surrounding countries) was implemented in December 2012. The forecast is published once a day at www.EcoForecast.EU. The air quality forecast is presented for ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, PM10 and PM2.5 as maps of daily maxima and daily averages. We will present results from the on-going model evaluation study over Central Europe (2010-2012). Modelling results were evaluated and compared with available observation of ozone and primary pollutants from air quality monitoring stations and from meteorological synoptic stations. Ozone exposure indices, as defined in the CAFE Directive, will be shown for the high resolution regional configuration.

Kaminski, Jacek W.; Struzewska, Joanna

2013-04-01

360

Forecast of stock market based on nonharmonic analysis used on NASDAQ since 1985  

Microsoft Academic Search

Although research involving economic time series forecasting based on virtual market models is frequently conducted, long-term forecasting is difficult due to many factors that affect actual markets. However, as exemplified by the business cycle and Elliot Wave theories in economics, it is assumed that fluctuations in economic time series forecasting have various periodicities, ranging from short-term to long-term. Accordingly, we

Takafumi Ichinose; Shigeki Hirobayashi; Tadanobu Misawa; Toshio Yoshizawa

2012-01-01

361

Gaussian Mixture Models for forecasting and filling of climatological time series  

NASA Astrophysics Data System (ADS)

In Statistics, a Gaussian Mixture Model (GMM) is a probabilistic density estimation method that consists of a linear combination of normal distributions. Model parameters, i.e. means and variances of each normal distribution, as well as the linear combination coefficients, can be estimated easily using the Expectation Maximization (EM) algorithm. Since the model expression is non-linear in its parameters (particularly regarding to the means and variances), GMMs are considered non-linear. In this work, a methodology to forecast time series based on a GMM is presented. Such model provides several advantages over other classical forecasting models, i.e. Autoregressive with exogenous variables (ARX) model. To forecast a multimodal variable (for example, temperature), GMM offers a more intuitive representation because each normal distribution can be adjusted over each mode. Besides, GMMs can be viewed as an ensemble of ARX models. Because of each ARX model can have different variance, the ensemble provides a similar behavior to an Autoregressive Conditional Heteroskedasticity (ARCH) model, allowing a better forecasting of the error band. The model has been applied for forecasting monthly time series of river flow in the Iberian Peninsula. The streamflow from a particular station is forecasted using the station itself (autoregression) and also the information provided by those stations with better correlation coefficients (exogenous variables). Data used in the model embraced the period between 1960-2005, using the interval 1960-1980 for calibration and 1961-2005 for validation. Results obtained highlight the usefulness of this methodology regarding to other classical forecasting models. This technique results particularly successful for filling missing data in the Iberian streamflow series. Acknowledgements: The Spanish Ministry of Science and Innovation, with additional support from the European Community Funds (FEDER), project CGL2007-61151/CLI, and the Regional Government of Andalusia project P06-RNM-01622, have financed this study.

Calandria-Hernández, D.; Hidalgo-Muñoz, J. M.; Argüeso, D.; Gámiz-Fortis, S. R.; Esteban-Parra, M. J.; Castro-Díez, Y.

2010-09-01

362

A Model of Quantum Economic Development  

Microsoft Academic Search

Quantum Economic Development (or the QED MODEL) is an entirely new field of theoretical economic conceptualisation into the evolutionary end point of the New Global Economy. A full description of the process of forming a kernel of fundamental 'quantum like' logic of the architecture and mechanics of these totally new quantum economies is included, as well as some of the

Craig Sobey

2009-01-01

363

Modelling the Economic Effects of Population Ageing  

Microsoft Academic Search

In March 2005, the Productivity Commission released a report on the Economic Implications of an Ageing Australia. The report describes projections for a number of economic variables including population, labour force participation rates, labour supply, employment and hours worked per week. The present paper describes a number of simulations with the MONASH model designed to extend the range of the

James Giesecke; G. A. Meagher

2008-01-01

364

Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts  

NASA Astrophysics Data System (ADS)

The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2011-12-01

365

Heterogeneous Agent Models in Economics and Finance  

Microsoft Academic Search

This paper surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect,

Cars H. Hommes

2005-01-01

366

GARCH modelling in association with FFT-ARIMA to forecast ozone episodes  

NASA Astrophysics Data System (ADS)

In operational forecasting of the surface O 3 by statistical modelling, it is customary to assume the O 3 time series to be generated through a homoskedastic process. In the present work, we've taken heteroskedasticity of the O 3 time series explicitly into account and have shown how it resulted in O 3 forecasts with improved forecast confidence intervals. Moreover, it also enabled us to make more accurate probability forecasts of ozone episodes in the urban areas. The study has been conducted on daily maximum O 3 time series for four urban sites of two major European cities, Brussels and London. The sites are: Brussels (Molenbeek) (B1), Brussels (PARL.EUROPE) (B2), London (Brent) (L1) and London (Bloomsbury) (L2). Fast Fourier Transform (FFT) has been used to model the periodicities (annual periodicity is especially distinct) exhibited by the time series. The residuals of "actual data subtracted with their corresponding FFT component" exhibited stationarity and have been modelled using ARIMA (Autoregressive Integrated Moving Average) process. The MAPEs (Mean absolute percentage errors) using FFT-ARIMA for one day ahead 100 out of sample forecasts, were obtained as follows: 20%, 17.8%, 19.7% and 23.6% at the sites B1, B2, L1 and L2. The residuals obtained through FFT-ARIMA have been modelled using GARCH (Generalized Autoregressive Conditional Heteroskedastic) process. The conditional standard deviations obtained using GARCH have been used to estimate the improved forecast confidence intervals and to make probability forecasts of ozone episodes. At the sites B1, B2, L1 and L2, 91.3%, 90%, 70.6% and 53.8% of the times probability forecasts of ozone episodes (for one day ahead 30 out of sample) have correctly been made using GARCH as against 82.6%, 80%, 58.8% and 38.4% without GARCH. The incorporation of GARCH also significantly reduced the no. of false alarms raised by the models.

Kumar, Ujjwal; De Ridder, Koen

2010-11-01

367

Combining forecast weights: Why and how?  

NASA Astrophysics Data System (ADS)

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

2012-09-01

368

Mean shifts, unit roots and forecasting seasonal time series  

Microsoft Academic Search

Examples of descriptive models for changing seasonal patterns in economic time series are autoregressive models with seasonal unit roots or with deterministic seasonal mean shifts. In this paper we show through a forecasting comparison for three macroeconomic time series (for which tests indicate the presence of seasonal unit roots) that allowing for possible seasonal mean shifts can improve forecast performance.

Richard Paap; Philip Hans Franses; Henk Hoek

1997-01-01

369

Climate information based streamflow and rainfall forecasts for Huai River Basin using Hierarchical Bayesian Modeling  

NASA Astrophysics Data System (ADS)

A Hierarchal Bayesian model for forecasting regional summer rainfall and streamflow season-ahead using exogenous climate variables for East Central China is presented. The model provides estimates of the posterior forecasted probability distribution for 12 rainfall and 2 streamflow stations considering parameter uncertainty, and cross-site correlation. The model has a multilevel structure with regression coefficients modeled from a common multivariate normal distribution results in partial-pooling of information across multiple stations and better representation of parameter and posterior distribution uncertainty. Covariance structure of the residuals across stations is explicitly modeled. Model performance is tested under leave-10-out cross-validation. Frequentist and Bayesian performance metrics used include Receiver Operating Characteristic, Reduction of Error, Coefficient of Efficiency, Rank Probability Skill Scores, and coverage by posterior credible intervals. The ability of the model to reliably forecast regional summer rainfall and streamflow season-ahead offers potential for developing adaptive water risk management strategies.

Chen, X.; Hao, Z.; Devineni, N.; Lall, U.

2013-09-01

370

Forecasting Alpine Vegetation Change Using Repeat Sampling and a Novel Modeling Approach  

Microsoft Academic Search

Global change affects alpine ecosystems by, among many effects, by altering plant distributions and community composition.\\u000a However, forecasting alpine vegetation change is challenged by a scarcity of studies observing change in fixed plots spanning\\u000a decadal-time scales. We present in this article a probabilistic modeling approach that forecasts vegetation change on Niwot\\u000a Ridge, CO using plant abundance data collected from marked

David R. Johnson; Diane Ebert-May; Patrick J. Webber; Craig E. Tweedie

2011-01-01

371

Simple but Effective: The OeNB’s Forecasting Model for Selected CESEE Countries  

Microsoft Academic Search

This paper describes the new forecasting tool used by the Oesterreichische Nationalbank (OeNB) to derive near-term forecasts for GDP and imports for five Central, Eastern and Southeastern European (CESEE) countries, namely Bulgaria, Croatia, the Czech Republic, Hungary and Poland. An error correction (EC) model is estimated separately for each country by means of seemingly unrelated regressions. Each country-specific macromodel consists

Jesús Crespo Cuaresma; Martin Feldkircher; Tomáš Sla?ík; Julia Wörz

2009-01-01

372

Statistical Model for Forecasting Monthly Large Wildfire Events in Western United States  

Microsoft Academic Search

The ability to forecast the number and location of large wildfire events (with specified confidence bounds) is important to fire managers attempting to allocate and distribute suppression efforts during severe fire seasons. This paper describes the development of a statistical model for assessing the forecasting skills of fire-danger predictors and producing 1-month-ahead wildfire-danger probabilities in the western United States. The

Haiganoush K. Preisler; Anthony L. Westerling

2007-01-01

373

Skills of different mesoscale models over Indian region during monsoon season: Forecast errors  

Microsoft Academic Search

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting\\u000a has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and\\u000a day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square\\u000a errors and specific events like the monsoon depressions.

Someshwar Das; Raghavendra Ashrit; Gopal Raman Iyengar; Saji Mohandas; M. Das Gupta; John P. George; E. N. Rajagopal; Surya Kanti Dutta

2008-01-01

374

Predictive fuzzy clustering model for natural streamflow forecasting  

Microsoft Academic Search

Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the natural streamflow. The streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. Currently, streamflow prediction using Box and Jenkins methodology prevails in the electric power industry.

M. H. Magalhaes; Rosangela Ballini; R. Goncalves; Femando Gomide

2004-01-01

375

Hybrid artificial intelligence methods in oceanographic forecast models  

Microsoft Academic Search

An approach to hybrid artificial intelligence problem solving is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. In situations in which the rules that determine a system are unknown or fuzzy, the prediction of the parameter values that determine the characteristic behavior of the system

Juan M. Corchado; Jim Aiken

2002-01-01

376

Objective pattern discrimination model for dust storm forecasting  

Microsoft Academic Search

For forecasting strong dust storms in the central and western areas of the Inner Mongolia Autonomous Region an objective discrimination approach has been developed. The 37 strong dust-storm events that occurred in this area during the last 40 years have been classified into four circulation patterns. Using the method suggested by Saaty (1996), a priority relationship matrix was set up

Gao Tao; Liu Jingtao; Yu Xiao; Kang Ling; Fan Yida; Hu Yinghua

2002-01-01

377

Wind speed and power forecasting based on spatial correlation models  

Microsoft Academic Search

Wind energy conversion systems (WECS) cannot be dispatched like conventional generators. This can pose problems for power system schedulers and dispatchers, especially if the schedule of wind power availability is not known in advance. However, if the wind speed can be reliably forecasted up to several hours ahead, the generating schedule can efficiently accommodate the wind generation. This paper illustrates

M. C. Alexiadis; P. S. Dokopoulos; H. S. Sahsamanoglou

1999-01-01

378

Alaska North Slope regional gas hydrate production modeling forecasts  

USGS Publications Warehouse

A series of gas hydrate development scenarios were created to assess the range of outcomes predicted for the possible development of the "Eileen" gas hydrate accumulation, North Slope, Alaska. Production forecasts for the "reference case" were built using the 2002 Mallik production tests, mechanistic simulation, and geologic studies conducted by the US Geological Survey. Three additional scenarios were considered: A "downside-scenario" which fails to identify viable production, an "upside-scenario" describes results that are better than expected. To capture the full range of possible outcomes and balance the downside case, an "extreme upside scenario" assumes each well is exceptionally productive.Starting with a representative type-well simulation forecasts, field development timing is applied and the sum of individual well forecasts creating the field-wide production forecast. This technique is commonly used to schedule large-scale resource plays where drilling schedules are complex and production forecasts must account for many changing parameters. The complementary forecasts of rig count, capital investment, and cash flow can be used in a pre-appraisal assessment of potential commercial viability.Since no significant gas sales are currently possible on the North Slope of Alaska, typical parameters were used to create downside, reference, and upside case forecasts that predict from 0 to 71??BM3 (2.5??tcf) of gas may be produced in 20 years and nearly 283??BM3 (10??tcf) ultimate recovery after 100 years.Outlining a range of possible outcomes enables decision makers to visualize the pace and milestones that will be required to evaluate gas hydrate resource development in the Eileen accumulation. Critical values of peak production rate, time to meaningful production volumes, and investments required to rule out a downside case are provided. Upside cases identify potential if both depressurization and thermal stimulation yield positive results. An "extreme upside" case captures the full potential of unconstrained development with widely spaced wells. The results of this study indicate that recoverable gas hydrate resources may exist in the Eileen accumulation and that it represents a good opportunity for continued research. ?? 2010 Elsevier Ltd.

Wilson, S. J.; Hunter, R. B.; Collett, T. S.; Hancock, S.; Boswell, R.; Anderson, B. J.

2011-01-01

379

DEMAND FORECASTING IN REGIONAL AIRPORTS: DYNAMIC TOBIT MODELS WITH GARCH ERRORS  

Microsoft Academic Search

In this paper we discuss the general issue of forecasting highly seasonal demand in regional airports, where peak flows approach airport capacity. For this, we propose a modeling combination, dynamic Tobit models with GARCH errors\\/disturbances, that is able to capture many of the shortcomings of most traditional models. Models are calibrated using monthly passenger and flight data for a 20

Matthew G. Karlaftis

2008-01-01

380

Modelling and forecasting long memory in exchange rate volatility vs. stable and integrated GARCH models  

Microsoft Academic Search

The purpose of this article is to compare stable, integrated and long-memory generalized autoregressive conditional heteroscedasticity (GARCH) models in forecasting the volatility of returns in the Turkish foreign exchange market for the period 1990–2005 and for the subperiod that covers the floating exchange rate regime 2001–2005. In the first period, we found that long-memory GARCH specifications capture the temporal pattern

I??l Akgül; Hülya Sayyan

2008-01-01

381

FOMC consensus forecasts  

Microsoft Academic Search

In November 2007, the Federal Open Market Committee (FOMC) announced a change in the way it communicates its view of the economic outlook: It increased the frequency of its forecasts from two to four times per year, and it increased the length of the forecasting horizon from two to three years. The FOMC does not release the individual members' forecasts

William T. Gavin; Geetanjali Pande

2008-01-01

382

Bayesian Model of Behaviour in Economic Games  

Microsoft Academic Search

Classical game theoretic approaches that make strong rationality assumptions have difficulty modeling human behaviour in economic games. We investigate the role of finite levels of iterated reasoning and non-selfish utility functions in a Partially Observable Markov Decision Process model that incorporates game theoretic no- tions of interactivity. Our generative model captures a broad class of characteristic behaviours in a multi-round

Debajyoti Ray; Brooks King-casas; P. Read Montague; Peter Dayan

2008-01-01

383

A Stochastic Deterministic Air Quality Forecasting System : Combining Time Series Models with Data-Assimilation  

NASA Astrophysics Data System (ADS)

A new air quality forecast system has been developed in which all the corrections for the air quality model output by assimilating observations have been carried out in post-processing mode. In order to make more accurate forecasts of the air pollutants, time series models have been used in combination with data-assimilation. The approach has been validated for one day ahead forecasts of daily mean PM10 and daily mean NO2. First, the air quality model AURORA has been applied over the domain Belgium including part of its neighbouring areas with grid resolution of 3×3 km2 for a total of 121×71 grids. The observations data from AIRBASE archive has been used for the assimilation purpose. Only the background stations (urban or rural) data has been used. For data-assimilation, optimal interpolation in conjunction with Hollingsworth-Lönnberg method has been applied. The time series of the residuals, i.e., observations minus model output (for the daily mean PM10 and NO2) has been collected for the grids where monitoring stations were available. These time series were tested for their suitability for time series modelling applications. We applied the ARIMA(p,d,q) (Autoregressive Integrated Moving Average) as time series modelling technique to forecast the residuals in the future (one day ahead). In the next step, these forecasted residuals were assimilated with forecasted AURORA model output in order to get improved forecasted fields. The validation was carried out by leaving three stations out in one run of data-assimilation/time series forecasting. Thus, the validation results for one day ahead forecasts at the 15 stations for the duration 1-Mar-07 to 31-Dec-07 reveal that there has been substantial improvement in root mean square error (RMSE), a reduction ranging from 2% to 30%, has been observed. Similarly, correlation has also increased upto 30%. The results show that the approach presented here has tremendous potential to be applied in air quality forecasts.

Kumar, U.; De Ridder, K.; Lefebvre, W.; Janssen, S.

2012-04-01

384

Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?  

NASA Astrophysics Data System (ADS)

In most previous works on forecasting oil market volatility, squared daily returns were taken as the proxy of unobserved actual volatility. However, as demonstrated by Andersen and Bollerslev (1998) [22], this proxy with too high measurement noise could be perfectly outperformed by a so-called realized volatility (RV) measure calculated by the cumulative sum of squared intraday returns. With this motivation, we further extend earlier works by employing intraday high-frequency data to compare the performance of three typical volatility models in the daily out-of-sample volatility forecasting of fuel oil futures on the Shanghai Futures Exchange (SHFE): the GARCH-type, stochastic volatility (SV) and realized volatility models. By taking RV as the proxy of actual daily volatility and then computing forecasting errors, we find that the realized volatility model based on intraday high-frequency data produces significantly more accurate volatility forecasts than the GARCH-type and SV models based on daily returns. Furthermore, the SV model outperforms many linear and nonlinear GARCH-type models that capture long-memory volatility and/or the asymmetric leverage effect in volatility. These results also prove that abundant volatility information is available in intraday high-frequency data, and can be used to construct more accurate oil volatility forecasting models.

Wei, Yu

2012-11-01

385

The Sheffield rheumatoid arthritis health economic model.  

PubMed

The Sheffield RA health economic model has been used in several published cost-effectiveness analyses in both the UK and internationally to evaluate different treatments for patients with RA. This article presents the key methods and assumptions that underpin the model, including justifications for using an individual patient sampling methodology, and why the model has used the HAQ to track disease activity. The article also details how trial and observational data are used in the model to address specific questions. The model has been used to support health policy in both the UK and internationally, although the limited evidence still provides a challenge when using an economic model to determine the cost-effectiveness of RA treatments. The results of analyses using the Sheffield RA model are presented. The limitations of the model are discussed, and improvements are continually required to provide a model that is appropriate to address health economic questions in the future. The Sheffield RA model continues to be used and refined, and allows health economic questions to be answered using a transparent and flexible modelling methodology. PMID:21859702

Tosh, Jonathan; Brennan, Alan; Wailoo, Allan; Bansback, Nick

2011-09-01

386

Preliminary Results of the Time-Dependent Earthquake Forecast Model MARFSTA applied to Mainland Japan  

NASA Astrophysics Data System (ADS)

The Gutenberg-Richter frequency magnitude distribution (Gutenberg and Richter, 1944) plays a major role in earthquake forecasting and hazards analysis. The parameters of this distribution are often assumed to be stationary. As earthquake forecasts are specified over a long period of time, short term irregularities in the parameter values are not of interest. However, we show that over a particular study region these parameters are in fact non-stationary, and that models with temporally variant parameter values describe the data significantly better than models assuming stationarity (Smyth and Mori, 2009). We attempt to model these temporal fluctuations in the Gutenberg-Richter distribution with an autoregressive technique to forecast the short term rate of earthquakes within a region of Japan. Over time, our model can be trained to detect increasing or decreasing rates based over a long history. The predicted forecast rate and frequency magnitude distribution are overlaid on a density map of the area obtained using a multivariate mixture model. The mixture model clusters historical earthquakes with no restraints placed on the size or shape of clusters. Overlaying the two models gives a spatially and temporally variant forecast of seismicity. We repeat the procedure for many regions to obtain a short term forecast for the Japanese mainland. The forecast model differs from currently proposed models by its assumption of non-stationarity and its density estimation. We also incorporate a further time-dependency component in our model by assuming that as time passes since the last large earthquake the probability of another larger earthquake increases. This requires the knowledge of the repeat times of earthquakes along a fault in order to estimate the probability distribution of repeat times. We cannot estimate the repeat times empirically owing to the long history required, so here we use a simulation approach based on the available historical data. The overall product is an earthquake forecast algorithm, MARFSTA, ready for real time testing. We present preliminary results of the model applied to mainland Japan. These results show that recent large earthquakes in Japan fall into regions of high probability indicated by our forecast.

Smyth, C. W.; Mori, J. J.

2009-12-01

387

The use of numerical weather forecast model predictions as a source of data for irrigation modelling  

NASA Astrophysics Data System (ADS)

The use of numerical weather forecast model data as a source of data for soil moisture modelling was tested. Results show that the potential evaporation calculated using the Penman-Monteith equation can be estimated accurately using data obtained from the output of a high resolution numerical atmospheric model (HIRLAM, High Resolution Limited Area Model). The mean bias error was 0.26 mm for a 36-hour sum and the root mean square error was 2.14 mm. The evaporation obtained directly from HIRLAM was systematically smaller because this direct model output represents the real evaporation rather than the potential evaporation. The precipitation forecasts were less accurate. When the accuracy of parameters required for the calculation of potential evaporation were studied for one station, no serious bias was found. When two different irrigation models (AMBAV and SWAP) were run over one summer using either measured or HIRLAM data as the input, the results given by the models were quite similar regardless of input data source. The largest differences between the model outputs were caused by the formulation of crop and soil characteristics in the irrigation models.

Venäläinen, A.; Salo, T.; Fortelius, C.

2005-12-01

388

Comparing Price Forecast Accuracy of Natural Gas Models andFutures Markets  

SciTech Connect

The purpose of this article is to compare the accuracy of forecasts for natural gas prices as reported by the Energy Information Administration's Short-Term Energy Outlook (STEO) and the futures market for the period from 1998 to 2003. The analysis tabulates the existing data and develops a statistical comparison of the error between STEO and U.S. wellhead natural gas prices and between Henry Hub and U.S. wellhead spot prices. The results indicate that, on average, Henry Hub is a better predictor of natural gas prices with an average error of 0.23 and a standard deviation of 1.22 than STEO with an average error of -0.52 and a standard deviation of 1.36. This analysis suggests that as the futures market continues to report longer forward prices (currently out to five years), it may be of interest to economic modelers to compare the accuracy of their models to the futures market. The authors would especially like to thank Doug Hale of the Energy Information Administration for supporting and reviewing this work.

Wong-Parodi, Gabrielle; Dale, Larry; Lekov, Alex

2005-06-30

389

Forecasting Flu  

MedlinePLUS

... regular feature of the annual flu season. Adapting Weather Models Flu forecasting adapts approaches used by meteorologists ... when meteorologists seem to get it wrong, but weather prediction is actually very good," says Jeffrey Shaman, ...

390

Innovation Forecasting.  

National Technical Information Service (NTIS)

Technological forecasting is premised on a certain orderliness of the innovation process. Myriad studies of technological substitution, diffusion, and transfer processes have yielded conceptual models of what matters for successful innovation. Yet most te...

A. L. Porter R. J. Watts

1997-01-01

391

Mathematical modeling in solving problems of the ice jams computation and forecasting  

Microsoft Academic Search

In this paper, a review of the modern state of the river ice jamming numerical modeling is given. Descriptions and algorithms\\u000a of mathematical models of ice jamming are presented. Questions related to the models practical implementation for the ice\\u000a jamming forecasting are considered.

V. A. Buzin

2009-01-01

392

Design of a next-generation regional weather research and forecast model  

Microsoft Academic Search

The Weather Research and Forecast (WRF) model is a new model development effort undertaken jointly by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and a number of collaborating institutions and university scientists. The model is intended for use by operational NWP and university research communities, providing a common framework for idealized dynamical studies,

J. Michalakes; J. Dudhia; D. Gill; J. Klemp; W. Skamarock

1999-01-01

393

Towards an Operational Sun-to-Earth Model for Space Weather Forecasting  

Microsoft Academic Search

We are presently developing a physics based, modular, large-scale model of the solar-terrestrial environment simulating space weather phenomena and providing a framework to test theories and explore the possibility of operational use in space weather forecasting. This talk will describe the main components of the model (a global MHD code, an upper atmosphere and ionosphere model, and the inner magnetosphere

T. I. Gombosi; C. R. Clauer; D. L. De Zeeuw; K. C. Hansen; W. B. Manchester; K. G. Powell; A. J. Ridley; I. Roussev; I. V. Sokolov; G. Toth; R. A. Wolf; S. Sazykin; T. E. Holzer; B. C. Low; A. D. Richmond; R. G. Roble

2002-01-01

394

Short Term Load Forecasting Models in Czech Republic Using Soft Computing Paradigms  

Microsoft Academic Search

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained from the Czech

Muhammad Riaz Khan; Ajith Abraham

2004-01-01

395

A Wavelet Network Model for Short-Term Traffic Volume Forecasting  

Microsoft Academic Search

Wavelet networks (WNs) are recently developed neural network models. WN models combine the strengths of discrete wavelet transform and neural network processing to achieve strong nonlinear approximation ability, and thus have been successfully applied to forecasting and function approximations. In this study, two WN models based on different mother wavelets are used for the first time for short-term traffic volume

Yuanchang Xie; Yunlong Zhang

2006-01-01

396

A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan  

Microsoft Academic Search

Three existing models are coupled to assess crop development and forecast yield in the largest contiguous irrigation network in the world: the Indus Basin in Pakistan. Monteith’s model is used for the calculation of absorbed photosynthetically active radiation (APAR), the Carnegie Institution Stanford model is used for determining the light use efficiency, and the surface energy balance algorithm for land

Wim G. M. Bastiaanssen; Samia Ali

2003-01-01

397

Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment  

Microsoft Academic Search

We study the modeling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of large datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalizing the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated

Rohit Deo; Clifford Hurvich; Yi Lu

2005-01-01

398

Forecasting Realised Volatility using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment  

Microsoft Academic Search

We study the modelling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalising the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as

Clifford Hirvich; Rohit S. Deo; Yi Luo

2003-01-01

399

A Fuzzy Logic Fog Forecasting Model for Perth Airport  

Microsoft Academic Search

Perth Airport is a major airport along the southwest coast of Australia. Even though, on average, fog only occurs about twelve\\u000a times a year, the lack of suitable alternate aerodromes nearby for diversion makes fog forecasts for Perth Airport very important\\u000a to long-haul international flights. Fog is most likely to form in the cool season between April and October. This

Y. Miao; R. Potts; X. Huang; G. Elliott; R. Rivett

2011-01-01

400

Short-term Forecasting Using Advanced Physical Modelling – The Results of the Anemos Project  

Microsoft Academic Search

Abstract Apossible solution to the problem of forecasting wind farms output in complex terrain sites comes in the form of high-resolution, advanced numerical flow models trying to improve ,on the ,NWP models ,shortcomings. These models can belinear flow models like Risø’s WAsP, or AriaWind, meso-scale models like the well-known MM5 community model, MeteoFrance’s MesoNH or IASA’s RAMS model, or full-blown

G. Giebel; J. Badger; I. Martí Perez; P. Louka; G. Kallos; A. M. Palomares; C. Lac

401

Economic Modeling in Chronic Obstructive Pulmonary Disease  

Microsoft Academic Search

Calculating the cost-effectiveness of interventions is an important step in accurately assessing the health and financial burdens of a disease. Although clinical trials that include cost data can be used to compare the cost-effectiveness of specific interventions, they only deal with outcomes within the time frame of the trial. Health economic models can synthesize epidemiologic, clinical, economic, andquality-of-lifedatafrommanydifferentsourcesandextrapolate results to

Maureen Rutten-van Molken; Todd A. Lee

2006-01-01

402

Playing Safe with Predictions: Hedging, Attribution and Conditions in Economic Forecasting.  

ERIC Educational Resources Information Center

|Approaches used by linguists to examine the way in which speakers or writers modify their commitment to the propositional content of their utterances are discussed, and it is noted that a frequent criticism is the failure of inexperienced speakers or writers to modulate their utterances properly. This paper considers economic reports and in…

Makaya, Pindi; Bloor, Thomas

403

Economic baselines for current underground coal mining technology. Final report. [Forecasting to 2000  

Microsoft Academic Search

This report describes the calculations of the cost of mining coal using a room and pillar mining method with continuous miner and a longwall mining system. The costs were calculated for the 1975 and year 2000 time periods and are to be used as economic standards against which advanced mining concepts and systems will be compared. The calculations procedure used

Mabe

1979-01-01

404

Application of tank, NAM, ARMA and neural network models to flood forecasting  

NASA Astrophysics Data System (ADS)

Two lumped conceptual hydrological models, namely tank and NAM and a neural network model are applied to flood forecasting in two river basins in Thailand, the Wichianburi on the Pasak River and the Tha Wang Pha on the Nan River using the flood forecasting procedure developed in this study. The tank and NAM models were calibrated and verified and found to give similar results. The results were found to improve significantly by coupling stochastic and deterministic models (tank and NAM) for updating forecast output. The neural network (NN) model was compared with the tank and NAM models. The NN model does not require knowledge of catchment characteristics and internal hydrological processes. The training process or calibration is relatively simple and less time consuming compared with the extensive calibration effort required by the tank and NAM models. The NN model gives good forecasts based on available rainfall, evaporation and runoff data. The black-box nature of the NN model and the need for selecting parameters based on trial and error or rule-of-thumb, however, characterizes its inherent weakness. The performance of the three models was evaluated statistically.

Tingsanchali, Tawatchai; Gautam, Mahesh Raj

2000-10-01

405

Reserve growth in oil pools of Alberta: Model and forecast  

USGS Publications Warehouse

Reserve growth is recognized as a major component of additions to reserves in most oil provinces around the world, particularly in mature provinces. It takes place as a result of the discovery of new pools/reservoirs and extensions of known pools within existing fields, improved knowledge of reservoirs over time leading to a change in estimates of original oil-in-place, and improvement in recovery factor through the application of new technology, such as enhanced oil recovery methods, horizontal/multilateral drilling, and 4D seismic. A reserve growth study was conducted on oil pools in Alberta, Canada, with the following objectives: 1) evaluate historical oil reserve data in order to assess the potential for future reserve growth; 2) develop reserve growth models/ functions to help forecast hydrocarbon volumes; 3) study reserve growth sensitivity to various parameters (for example, pool size, porosity, and oil gravity); and 4) compare reserve growth in oil pools and fields in Alberta with those from other large petroleum provinces around the world. The reported known recoverable oil exclusive of Athabasca oil sands in Alberta increased from 4.5 billion barrels of oil (BBO) in 1960 to 17 BBO in 2005. Some of the pools that were included in the existing database were excluded from the present study for lack of adequate data. Therefore, the known recoverable oil increased from 4.2 to 13.9 BBO over the period from 1960 through 2005, with new discoveries contributing 3.7 BBO and reserve growth adding 6 BBO. This reserve growth took place mostly in pools with more than 125,000 barrels of known recoverable oil. Pools with light oil accounted for most of the total known oil volume, therefore reflecting the overall pool growth. Smaller pools, in contrast, shrank in their total recoverable volumes over the years. Pools with heavy oil (gravity less than 20o API) make up only a small share (3.8 percent) of the total recoverable oil; they showed a 23-fold growth compared to about 3.5-fold growth in pools with medium oil and 2.2-fold growth in pools with light oil over a fifty-year period. The analysis indicates that pools with high porosity reservoirs (greater than 30 percent porosity) grew more than pools with lower porosity reservoirs which could possibly be attributed to permeability differences between the two types. Reserve growth models for Alberta, Canada, show the growth at field level is almost twice as much as at pool level, possibly because the analysis has evaluated fields with two or more pools with different discovery years. Based on the models, the growth in oil volumes in Alberta pools over the next five-year period (2006-2010) is expected to be about 454 million barrels of oil. Over a twenty-five year period, the cumulative reserve growth in Alberta oil pools has been only 2-fold compared to a 4- to- 5-fold increase in other petroleum producing areas such as Saskatchewan, Volga-Ural, U.S. onshore fields, and U.S. Gulf of Mexico. However, the growth at the field level compares well with that of U.S. onshore fields. In other petroleum provinces, the reserves are reported at field levels rather than at pool levels, the latter basically being the equivalent of individual reservoirs. ?? 2010 by the Canadian Society of Petroleum Geologists.

Verma, M.; Cook, T.

2010-01-01

406

AFWA's Space Weather Modeling System: A Flexible Space Weather Forecast System  

NASA Astrophysics Data System (ADS)

A key requirement of models used for space weather forecasting is making them flexible enough to exploit new computational capabilities as technology advances. This flexibility occurs when models are made scalable and portable while maintaining their existing capabilities and accuracy. Scalability allows the models to run faster as more processors are added. Portability enables the models to run on a variety of computing platforms. This makes operational procurement decisions more flexible and cost-effective. The Battlespace Environments Institute (BEI) project supports the coupling of Earth system environment models, such as oceans and atmospheres together, under the Earth System Modeling Framework (ESMF). The project mandates scalability and portability of the coupled models to adapt readily to changing computational environments. The Space Weather Modeling System (SWMS) is a BEI model of solar-terrestrial space weather. The Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and the Ionospheric Forecast Model (IFM) are the first two coupled components in SWMS. The HAFv2 model produces quantitative forecasts of solar wind parameters at Earth and elsewhere in the inner heliosphere. The IFM is the physics-based ionosphere model of Global Assimilation of Ionospheric Measurements (GAIM) data-assimilation model. IFM provides highly representative specifications of plasma conditions in the global ionosphere. Coupling these two models together in the SWMS enables multi-day forecasts of solar wind and ionospheric disturbances. SWMS is an example of a successful transition of research to operations that is flexible while maintaining accuracy. This capability is crucial to DoD because it provides their warfighters with the actionable space weather forecasts that they need to make operational decisions. We present the solar wind and ionospheric results of the SWMS model for the large solar storm of April 6-7, 2000 with comparisons to solar wind and ionospheric data.

Fry, C. D.; Eccles, J.; Reich, J. P.; Berman, L. M.; Sattler, M. P.

2008-12-01

407

On Equilibrium Growth of Dynamic Economic Models.  

National Technical Information Service (NTIS)

The paper studies equilibrium growth of economic models. In the first part a rather general open ended multi-sector model is introduced and its possible steady states are characterized. These are shown to belong to one of two types. They are either golden...

D. Gale

1971-01-01

408

An Economic Model for Selective Admissions  

ERIC Educational Resources Information Center

The author presents an economic model for selective admissions to postsecondary nursing programs. Primary determinants of the admissions model are employment needs, availability of educational resources, and personal resources (ability and learning potential). As there are more applicants than resources, selective admission practices are…

Haglund, Alma

1978-01-01

409

Ecological economic modeling and valuation of ecosystems  

Microsoft Academic Search

We are attempting to integrate ecological and economic modeling and analysis in order to improve our understanding of regional systems, assess potential future impacts of various land-use, development, and agricultural policy options, and to better assess the value of ecological systems. Starting with an existing spatially articulated ecosystem model of the Patuxent River drainage basin in Maryland, we are adding

N. Bockstael; R. Costanza; I. Strand; W. Boynton; K. Bell; L. Wainger

1995-01-01

410

Economic Foundations of LEAP Model 22C.  

National Technical Information Service (NTIS)

The model for long-term energy projections, the DOE/EIA's Long Term Energy Analysis Program (LEAP), is discussed. This report identifies and presents an initial assessment of the major underlying economic assumptions of LEAP Model 22C, the version of LEAP...

J. A. Hansen M. Becker J. L. Trimble

1981-01-01

411

Results of the Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California  

PubMed Central

The Regional Earthquake Likelihood Models (RELM) test of earthquake forecasts in California was the first competitive evaluation of forecasts of future earthquake occurrence. Participants submitted expected probabilities of occurrence of M?4.95 earthquakes in 0.1° × 0.1° cells for the period 1 January 1, 2006, to December 31, 2010. Probabilities were submitted for 7,682 cells in California and adjacent regions. During this period, 31 M?4.95 earthquakes occurred in the test region. These earthquakes occurred in 22 test cells. This seismic activity was dominated by earthquakes associated with the M = 7.2, April 4, 2010, El Mayor–Cucapah earthquake in northern Mexico. This earthquake occurred in the test region, and 16 of the other 30 earthquakes in the test region could be associated with it. Nine complete forecasts were submitted by six participants. In this paper, we present the forecasts in a way that allows the reader to evaluate which forecast is the most “successful” in terms of the locations of future earthquakes. We conclude that the RELM test was a success and suggest ways in which the results can be used to improve future forecasts.

Lee, Ya-Ting; Turcotte, Donald L.; Holliday, James R.; Sachs, Michael K.; Rundle, John B.; Chen, Chien-Chih; Tiampo, Kristy F.

2011-01-01

412

The systematic study of the stability of forecasts in the rate- and state-dependent model  

NASA Astrophysics Data System (ADS)

Numerous observations have shown a general spatial correlation between positive Coulomb failure stress changes due to an earthquake and the locations of aftershocks. However this correlation does not give any indication of the rate from which we can infer the magnitude using the Gutenberg-Richter law. Dieterich's rate- and state-dependent model can be used to obtain a forecast of the observed aftershock rate for the space and time evolution of seismicity caused by stress changes applied to an infinite population of nucleating patches. The seismicity rate changes on this model depend on eight parameters: the stressing rate, the amplitude of the stress perturbation, the physical constitutive properties of faults, the spatial parameters (location and radii of the cells), the start and duration of each of the temporal windows as well as the background seismicity rate. The background seismicity is declustered using the epidemic type aftershock sequence model. We use the 1992 Landers earthquake as a case study, using the Southern California Earthquake Data Centre (SCEDC) catalogue, to examine if Dieterich's rate- and state-dependent model can forecast the aftershock seismicity rate. We perform a systematic study on a range of values on all the parameters to test the forecasting ability of this model. The results obtained suggest variable success in forecasting, when varying the values for the parameters, with the spatial and temporal parameters being the most sensitive. The Omori-Utsu law describes the aftershock rate as a power law in time following the main shock and depends on only three parameters: the aftershock productivity, the elapsed time since the main shock and the constant time shift, all of which can be estimated in the early part of the aftershock sequence and then extrapolated to give a long term rate forecast. All parameters are estimated using maximum likelihood methods. We compare the Dieterich and the Omori-Utsu forecasts using the Akaike information criterion which appropriately penalises each model for the number of free parameters used in the fit and explore the full spatial distribution of parameters, forecasts and forecast skill. We find that the Omori-Utsu law consistently out-performs the Dieterich model. We then apply the method to other earthquake sequences and assess its usefulness as a real time aftershock forecasting protocol.

De Gaetano, D.; McCloskey, J.; Nalbant, S. S.

2011-12-01

413

Forecasted Electric Energy Consumption and Peak Demands for Maryland.  

National Technical Information Service (NTIS)

This report presents the results of an economic forecast of electric energy consumption in Maryland through 2010. Summer and winter peak demand projections through 2010 are also presented. Separate econometric models were developed for residential, commer...

S. Estomin E. Nicholson M. Lee

2003-01-01

414

An enhanced PM 2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations  

NASA Astrophysics Data System (ADS)

An enhanced PM 2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM 2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM 2.5 air quality is more likely to be critical for human health. The enhanced PM 2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM 2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM 2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.

Cobourn, W. Geoffrey

2010-08-01

415

Economic models of employee motivation  

Microsoft Academic Search

Workers present employers with a range of tricky problems. They can be crooked, subversive, surly, or indolent, even if they are paid on time. Joseph A. Ritter and Lowell J. Taylor explore economists' main theories of how compensation is used to address employee motivation and how these models help to explain puzzling features of labor market. Although these theories are

Joseph A. Ritter; Lowell J. Taylor

1997-01-01

416

Martingales and efficient forecasts of effective mortgage rates  

Microsoft Academic Search

Economic theory and empirical evidence indicate that the optimal near-term forecast of a long-term rate is (approximately) today's rate; the no-change model should provide excellent near-term forecasts of a long-term rate. This article estimates the longest forecast horizon over which no-change predictions of each of three mortgage-related interest rates pass a series of quality tests. The empirical results reject the

William Reichenstein

1989-01-01

417

Weather Forecasting  

NSDL National Science Digital Library

Weather Forecasting is a set of computer-based learning modules that teach students about meteorology from the point of view of learning how to forecast the weather. The modules were designed as the primary teaching resource for a seminar course on weather forecasting at the introductory college level (originally METR 151, later ATMO 151) and can also be used in the laboratory component of an introductory atmospheric science course. The modules assume no prior meteorological knowledge. In addition to text and graphics, the modules include interactive questions and answers designed to reinforce student learning. The module topics are: 1. How to Access Weather Data, 2. How to Read Hourly Weather Observations, 3. The National Collegiate Weather Forecasting Contest, 4. Radiation and the Diurnal Heating Cycle, 5. Factors Affecting Temperature: Clouds and Moisture, 6. Factors Affecting Temperature: Wind and Mixing, 7. Air Masses and Fronts, 8. Forces in the Atmosphere, 9. Air Pressure, Temperature, and Height, 10. Winds and Pressure, 11. The Forecasting Process, 12. Sounding Diagrams, 13. Upper Air Maps, 14. Satellite Imagery, 15. Radar Imagery, 16. Numerical Weather Prediction, 17. NWS Forecast Models, 18. Sources of Model Error, 19. Sea Breezes, Land Breezes, and Coastal Fronts, 20. Soundings, Clouds, and Convection, 21. Snow Forecasting.

Nielsen-Gammon, John

1996-09-01

418

Earthquake forecasting using the rate-and-state friction model and a smoothing Kernel: application to Taiwan  

NASA Astrophysics Data System (ADS)

In this work, two approaches were employed for estimating the spatiotemporal distribution of seismicity density in Taiwan. With the use of the rate-and-state friction model, a model for short-term forecasting according to the fault-interaction-based rate disturbance due to seismicity was considered. Another long-term forecasting model that involves a smoothing Kernel function is proposed. The application of the models to Taiwan led to good agreement between the model forecast and actual observations. Using an integration of the two approaches, the application was found to be capable of providing a seismicity forecast with a higher accuracy and reliability. To check the stability related to the regression the bandwidth function, the forecasted seismicity rates corresponding to the upper and lower bounds of the 95% confidence intervals are compared. The result shows that deviations within the bandwidth functions had an insignificant impact on forecasting reliability. Besides, insignificant differences in the forecasted rate change were obtained when A? was assumed to be between 0.1 and 0.4 bars for the application of the rate-and-state friction model. By considering the maximum Coulomb stress change among the seismogenic depth, the model presents a better forecasting ability than that using any single fixed target depth. The proposed methodology, with verified applicability for seismicity forecasts, could be useful for seismic hazard analyses.

Chan, C.-H.; Wu, Y.-M.; Wang, J.-P.

2012-10-01

419

An overview of forecast models evaluation for monitoring air quality management in the State of Texas, USA  

Microsoft Academic Search

Purpose – The purpose of this study is to investigate forecast models using data provided by the Texas Commission on Environmental Quality (TCEQ) to monitor and develop forecast models for air quality management. Design\\/methodology\\/approach – The models used in this research are the LDF (Fisher Linear Discriminant Function), QDF (Quadratic Discriminant Function), REGF (Regression Function), BPNN (Backprop Neural Network), and

A. B. M. Abdullah; David Mitchell; Robert Pavur

2009-01-01

420

Infraestructure forecast modelling II; Policy planning via structural analysis and balanced scorecard. Electricity in Colombia case study  

Microsoft Academic Search

Countless developments in forecasting models and processes have been developed in the last four decades, to support increasing demands in infrastructure services delivery and competitiveness. A wide range of these developments is available nowadays from highly detailed macroeconomic or technical forecasting models based on convergence of marginal functions, up to strategic business models supported on broad and soft methods of

2007-01-01

421

Emerging Trends of Computational Grid Based Near Real Time\\/Real Time Flood Assessment and Forecasting Models  

Microsoft Academic Search

From recent past, the computational Grid based flood assessment and forecasting models is getting emerged as an interdisciplinary integrated `near real time\\/real time model'. Many such, Grid based flood assessment and forecasting model supports in logically integrating various components of flood related scientific simulations such as Metrological, Hydrological, Hydraulic, RADAR, LIDAR Remote Sensing, GIS, Satellite Communication and other technologies and

M. Manavalan; S. Chattopadhyay; M. Mangala; Y. S. Rao

2010-01-01

422

Forecasting Taiwan's GDP by the novel nash nonlinear grey Bernoulli model with trembling-hand perfect equilibrium  

NASA Astrophysics Data System (ADS)

The traditional grey forecasting model, GM (1, 1), is criticized for its unsatisfied forecasting accuracy. The Nash Nonlinear Grey Bernoulli Model (NNGBM (1, 1)) is proposed and proven with high prediction accuracy. Because of the multiple Nash solutions, this study uses trembling-hand perfect equilibrium to refine the NNGBM and then obtain higher forecasting accuracy. This study mathematically proves that the proposed model is feasible and efficient. Finally, NNGBM with trembling-hand perfect equilibrium is used to forecast Taiwan's GDP. The results show Taiwan's GDP is keeping on growing.

Hsin, Pei-Han

2013-09-01

423

Utilizing Operational and Improved Remote Sensing Measurements to Assess Air Quality Monitoring Model Forecasts  

NASA Astrophysics Data System (ADS)

Air quality model forecasts from Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) are often used to support air quality applications such as regulatory issues and scientific inquiries on atmospheric science processes. In urban environments, these models become more complex due to the inherent complexity of the land surface coupling and the enhanced pollutants emissions. This makes it very difficult to diagnose the model, if the surface parameter forecasts such as PM2.5 (particulate matter with aerodynamic diameter less than 2.5 microm) are not accurate. For this reason, getting accurate boundary layer dynamic forecasts is as essential as quantifying realistic pollutants emissions. In this thesis, we explore the usefulness of vertical sounding measurements on assessing meteorological and air quality forecast models. In particular, we focus on assessing the WRF model (12km x 12km) coupled with the CMAQ model for the urban New York City (NYC) area using multiple vertical profiling and column integrated remote sensing measurements. This assessment is helpful in probing the root causes for WRF-CMAQ overestimates of surface PM2.5 occurring both predawn and post-sunset in the NYC area during the summer. In particular, we find that the significant underestimates in the WRF PBL height forecast is a key factor in explaining this anomaly. On the other hand, the model predictions of the PBL height during daytime when convective heating dominates were found to be highly correlated to lidar derived PBL height with minimal bias. Additional topics covered in this thesis include mathematical method using direct Mie scattering approach to convert aerosol microphysical properties from CMAQ into optical parameters making direct comparisons with lidar and multispectral radiometers feasible. Finally, we explore some tentative ideas on combining visible (VIS) and mid-infrared (MIR) sensors to better separate aerosols into fine and coarse modes.

Gan, Chuen-Meei

424

Regional Sources of Error Growth in the National Meteorological Center's Medium-Range Forecast Model  

NASA Astrophysics Data System (ADS)

The full spatial structure of systematic and random error growth in the National Meteorological Center's Medium -Range Forecast Model is investigated in an effort to identify the sources of error growth. The random error growth is partitioned into two types: external error growth, which is due to model deficiencies, and internal error growth, which is the unstable growth of errors in the initial conditions. Data from winter 1987, summer 1990 and winter 1992 are compared to assess seasonal variations in regional error growth, as well as to forecast model improvement. (1) The tropical upper-tropospheric easterly bias in the forecast model is strongest in the regions of the upper-tropospheric westerlies. The structure of the evolution with forecast time of the U -field variance is consistent with the theory of stronger meridional propagation of extratropical disturbances where the U-field is westerly and energy accumulation where delta U/delta x is negative. (2) The spatial structure of the external error growth in the extratropics reveals that the representation of orography in the model is inadequate. There is evidence of enhanced external error growth over the Rockies, Himalayas, and Antarctica. (3) In the tropics, high external error at the 200 mb level is closely associated with deep convection. There is evidence of significant model improvements in the tropics at the 850 mb level between 1987 and 1992. (4) Internal error growth in the mid-latitudes is strongly associated with blocking phenomena, especially over the North Atlantic and Europe. (5) In the tropics, there is some evidence of internal error accumulation in regions where delta U/delta x is negative. The results of this study are physically meaningful and in agreement with previous predictability studies, as well as current knowledge of forecast model deficiencies, while at the same time, providing new information about the full spatial and temporal distributions of forecast errors.

Reynolds, Carolyn A.

425

Evaluation of DNI forecast based on the WRF mesoscale atmospheric model for CPV applications  

NASA Astrophysics Data System (ADS)

The integration of large-scale solar electricity production into the energy supply structures depends es-sentially on the precise advance knowledge of the available resource. Numerical weather prediction (NWP) models provide a reliable and comprehensive tool for short-and medium-range solar radiation forecasts. The methodology followed here is based on the WRF model. For CPV systems the primary energy source is the direct normal irradi-ance (DNI), which is dramatically affected by the presence of clouds. Therefore, the reliability of DNI forecasts is directly related to the accuracy of cloud information. Two aspects of this issue are discussed here: (i) the effect of the model's horizontal spatial resolution; and (ii) the effect of the spatial aggregation of the predicted irradiance. Results show that there is no improvement in DNI forecast skill at high spatial resolutions, except under clear-sky conditions. Furthermore, the spatial averaging of the predicted irradiance noticeably reduces their initial error.

Lara-Fanego, V.; Ruiz-Arias, J. A.; Pozo-Vázquez, A. D.; Gueymard, C. A.; Tovar-Pescador, J.

2012-10-01

426

Improving a stage forecasting Muskingum model by relating local stage and remote discharge  

NASA Astrophysics Data System (ADS)

Following the parsimonious concept of parameters, simplified models for flood forecasting based only on flood routing have been developed for flood-prone sites located downstream of a gauged station and at a distance allowing an appropriate forecasting lead-time. In this context, the Muskingum model can be a useful tool. However, critical points in hydrological routing are the representation of lateral inflows contribution and the knowledge of stage-discharge relationships. As regards the former, O'Donnell (O'Donnell, T., 1985. A direct three-parameter Muskingum procedure incorporating lateral inflow, Hydrol. Sci. J., 30[4/12], 479-496) proposed a three-parameter Muskingum procedure assuming the lateral inflows proportional to the contribution entering upstream. Using this approach, Franchini and Lamberti (Franchini, M. & Lamberti, P., 1994. A flood routing Muskingum type simulation and forecasting model based on level data alone, Water Resour. Res., 30[7], 2183-2196) presented a simple model Muskingum type to provide forecast water levels at the downstream end by selecting a routing time interval and, hence, a forecasting lead-time allowing to express the forecast stage as a function of only observed quantities. Moramarco et al. (Moramarco, T., Barbetta, S., Melone, F. & Singh, V.P., 2006. A real-time stage Muskingum forecasting model for a site without rating curve, Hydrol. Sci. J., 51[1], 66-82) enhanced the modeling scheme incorporating a procedure for adapting the parameter linked to lateral inflows. This last model, called STAFOM (STAge FOrecasting Model), was also extended to a two connected river branches schematization in order to improve significantly the forecasting lead-time. The STAFOM model provided satisfactory results for most of the analysed flood events observed in different river reaches in the Upper-Middle Tiber River basin in Central Italy. However, the analysis highlighted that the stage forecast should be enhanced when sudden modifications occur in the upstream and downstream hydrographs recorded in real-time. Moramarco et al. (Moramarco, T., Barbetta, S., F. Melone, F. & Singh, V.P., 2005. Relating local stage and remote discharge with significant lateral inflow, J. Hydrol. Engng ASCE, 10[1], 58-69) showed that for any flood condition at ends of a river reach, a direct proportionality between the upstream and downstream mean velocity can be found. This insight was the basis for developing the Rating Curve Model (RCM) that allows to also accommodate significant lateral inflow contributions, permitting, without using a flood routing procedure and without the need of a rating curve at a local site, to relate the local hydraulic conditions with those at a remote gauged section. Therefore, to improve the STAFOM performance mainly for highly varying flood conditions, the model has been here modified by coupling it with a procedure based on the RCM approach. Several flood events occurred along different equipped river reaches of the Upper Tiber River basin have been used as case study. Results showed that the new model, named STAFOM-RCM, apart from to improve the stage forecast accuracy in terms of error on peak stage, Nash-Sutcliffe efficiency coefficient and the coefficient of persistence, allowed to use a larger lead time thus avoiding the two-river branches cascade schematization where fluctuations in stage forecasting occur more frequently.

Barbetta, S.; Moramarco, T.; Melone, F.; Brocca, L.

2009-04-01

427

Innovations in Sales Forecasting for Large-Scale Retailers  

Microsoft Academic Search

Working as a team for the Center for Business and Economic Research at the University of Southern Maine, Bruce, James, Lindsey, Brooks and Joseph created a forecasting system for a large retail chain. Their base model uses the ARIMA methodology of Box and Jenkins, but the team has extended ARIMA to deal with the significant challenges of forecasting weekly sales

Bruce Andrews; James Bennett; Lindsey Howe; Brooks Newkirk; Joseph Ogrodowczyk

2008-01-01

428

Multi-model multi-analysis ensembles in quasi-operational medium-range forecasting  

NASA Astrophysics Data System (ADS)

Ensemble prediction systems (EPS) for medium-range forecasting attempt to account for uncertainty in numerical weather prediction (NWP) by sampling the distribution function of future atmospheric states. Forecast uncertainty derives from uncertainty in both the analysed initial conditions (analysis errors) and in the forecast evolution (model errors). Current operational systems are primarily based on sampling the analysis errors through initial-condition perturbations with, at best, only limited sampling of model errors. One approach to sampling model errors and also to widening the sampling of analysis errors, is to include more than one NWP model, and more than one operational analysis to which perturbations are added, in the ensemble system. Previous work has demonstrated from a small number of case-studies that this multi-model multi-analysis ensemble (MMAE) approach can perform significantly better than a single-model system such as the Ensemble Prediction System (EPS) run by the ECMWF (European Centre for Medium-Range Weather Forecasts). In this study a MMAE was created by combining the ECMWF ensemble with an ensemble using the Met Office model and analysis, and was run daily for a year to assess the benefits over a larger, quasi-operational sample of forecasts. The results are compared with the operational ECMWF EPS which includes the latest upgrades, including stochastic physics which makes some allowance for uncertainty due to model errors. Results show that both for probabilistic forecasts (assessed by Brier skill scores and relative operating characteristics) and for deterministic forecasts based on the ensemble mean (assessed by root-mean square errors) the MMAE has increased forecast skill relative to the EPS. These improvements are obtained with no overall increase in ensemble size. Ensemble spread is also greater in the MMAE, and the increased skill is believed to be due to the additional model producing solutions which are synoptically more different than those produced by a single model ensemble. Benefits of the MMAE vary both in time and with geographical region, depending on which individual ensemble system performs better in particular synoptic situations. It is found that the MMAE almost always performs as well as the best individual ensemble, and on occasions better than either of them.

Mylne, Kenneth R.; Evans, Ruth E.; Clark, Robin T.

2002-01-01

429

Interevent times in a new alarm-based earthquake forecasting model  

NASA Astrophysics Data System (ADS)

This study introduces a new earthquake forecasting model that uses the moment ratio (MR) of the first to second order moments of earthquake interevent times as a precursory alarm index to forecast large earthquake events. This MR model is based on the idea that the MR is associated with anomalous long-term changes in background seismicity prior to large earthquake events. In a given region, the MR statistic is defined as the inverse of the index of dispersion or Fano factor, with MR values (or scores) providing a biased estimate of the relative regional frequency of background events, here termed the background fraction. To test the forecasting performance of this proposed MR model, a composite Japan-wide earthquake catalogue for the years between 679 and 2012 was compiled using the Japan Meteorological Agency catalogue for the period between 1923 and 2012, and the Utsu historical seismicity records between 679 and 1922. MR values were estimated by sampling interevent times from events with magnitude M ? 6 using an earthquake random sampling (ERS) algorithm developed during previous research. Three retrospective tests of M ? 7 target earthquakes were undertaken to evaluate the long-, intermediate- and short-term performance of MR forecasting, using mainly Molchan diagrams and optimal spatial maps obtained by minimizing forecasting error defined by miss and alarm rate addition. This testing indicates that the MR forecasting technique performs well at long-, intermediate- and short-term. The MR maps produced during long-term testing indicate significant alarm levels before 15 of the 18 shallow earthquakes within the testing region during the past two decades, with an alarm region covering about 20 per cent (alarm rate) of the testing region. The number of shallow events missed by forecasting was reduced by about 60 per cent after using the MR method instead of the relative intensity (RI) forecasting method. At short term, our model succeeded in forecasting the occurrence region of the 2011 Mw 9.0 Tohoku earthquake, whereas the RI method did not. Cases where a period of quiescent seismicity occurred before the target event often lead to low MR scores, meaning that the target event was not predicted and indicating that our model could be further improved by taking into account quiescent periods in the alarm strategy.

Talbi, Abdelhak; Nanjo, Kazuyoshi; Zhuang, Jiancang; Satake, Kenji; Hamdache, Mohamed

2013-09-01

430

Current challenges using models to forecast seawater intrusion: lessons from the Eastern Shore of Virginia, USA  

NASA Astrophysics Data System (ADS)

A three-dimensional model of the aquifer system of the Eastern Shore of Virginia, USA was calibrated to reproduce historical water levels and forecast the potential for saltwater intrusion. Future scenarios were simulated with two pumping schemes to predict potential areas of saltwater intrusion. Simulations suggest that only a few wells would be threatened with detectable salinity increases before 2050. The objective was to examine whether salinity increases can be accurately forecast for individual wells with such a model, and to address what the challenges are in making such model forecasts given current (2009) simulation capabilities. The analysis suggests that even with current computer capabilities, accurate simulations of concentrations within a regional-scale (many km) transition zone are computationally prohibitive. The relative paucity of data that is typical for such regions relative to what is needed for accurate transport simulations suggests that even with an infinitely powerful computer, accurate forecasting for a single well would still be elusive. Useful approaches may include local-grid refinement near wells and geophysical surveys, but it is important to keep expectations for simulated forecasts at wells in line with chloride concentration and other data that can be obtained at that local scale.

Sanford, Ward E.; Pope, Jason P.

2010-02-01

431

Chronological Reliability Model Incorporating Wind Forecasts to Assess Wind Plant Reserve Allocation: Preprint  

Microsoft Academic Search

Over the past several years, there has been considerable development and application of wind forecasting models. The main purpose of these models is to provide grid operators with the best information available so that conventional power generators can be scheduled as efficiently and as cost-effectively as possible. One of the important ancillary services is reserves, which involves scheduling additional capacity

Michael Milligan

2002-01-01

432

Real-time deployment of artificial neural network forecasting models: Understanding the range of applicability  

NASA Astrophysics Data System (ADS)

When an operational artificial neural network (ANN) model is deployed, new input patterns are collected in order to make real-time forecasts. However, ANNs (like other empirical and statistical methods) are unable to reliably extrapolate beyond the calibration range. Consequently, when deployed in real-time operation there is a need to determine if new input patterns are representative of the data used in calibrating the model. To address this problem, a novel detection system for identifying uncharacteristic data patterns is presented. This approach combines a self-organizing map (SOM), to partition the data set, with nonparametric kernel density estimators to calculate local density estimates (LDE). The SOM-LDE method determines the degree to which a new input pattern can be considered to be contained within the domain of the calibration set. If a new pattern is found to be uncharacteristic, a warning can be issued with the forecast, and the ANN model retrained to include the new pattern. This approach of selectively retraining the model is compared to no retraining and the more computationally onerous case of retraining the model after each new sample. These three approaches are applied to forecast flow in the Kentucky River, USA, using multilayer perceptron (MLP) models. The results demonstrate that there is a significant advantage in retraining an ANN that has been deployed as a real-time, operational model, and that the SOM-LDE classifier is an effective approach for identifying the model's range of applicability and assessing the usefulness of the forecast.

Bowden, Gavin J.; Maier, Holger R.; Dandy, Graeme C.

2012-10-01

433

Continuous hydrological modelling in the context of real time flood forecasting in alpine Danube tributary catchments  

Microsoft Academic Search

A hydrological modelling framework applied within operational flood forecasting systems in three alpine Danube tributary basins, Traisen, Salzach and Enns, is presented. A continuous, semi-distributed rainfall-runoff model, accounting for the main hydrological processes of snow accumulation and melt, interception, evapotranspiration, infiltration, runoff generation and routing is set up. Spatial discretization relies on the division of watersheds into subbasins and subsequently

Philipp Stanzel; Bianca Kahl; Ulrich Haberl; Mathew Herrnegger; H. P. Nachtnebel

2008-01-01

434

Forecasting fluid milk package type with a multigeneration new product diffusion model  

Microsoft Academic Search

A recently introduced multigeneration model, developed for high-technology industries and tested on a high-tech product class, is used to forecast use of three generations of packaging technology in the fluid milk market: glass, paperboard cartons, and plastic. Results show that the model can be successfully applied to industries not usually associated with high technology, and to specific markets, rather than

Mark W. Speece; Douglas L. MacLachlan

1992-01-01

435

Forecasting exchange rate volatility using conditional variance models selected by information criteria  

Microsoft Academic Search

This paper uses appropriately modified information criteria to select models from the GARCH family, which are subsequently used for predicting US dollar exchange rate return volatility. The out of sample forecast accuracy of models chosen in this manner compares favourably on mean absolute error grounds, although less favourably on mean squared error grounds, with those generated by the commonly used

Chris Brooks; Simon P. Burke

1998-01-01

436

Using neural networks and GIS to forecast land use changes: a Land Transformation Model  

Microsoft Academic Search

The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTMparameterized for Michigan's Grand Traverse Bay Watershed and explores how factors

Bryan C. Pijanowski; Daniel G. Brown; Bradley A. Shellito; Gaurav A. Manik

437

A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures  

Microsoft Academic Search

The primary objective of this paper is to develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data. The paper also seeks to provide insights into the impact of distribution policies on the adoption of new products. The model is intended to assist motion picture exhibitor chains (retailers) in managing

Mohanbir S. Sawhney; Jehoshua Eliashberg

1996-01-01

438

Simulation of Water Cycle With a Martian Weather Research and Forecast Model  

Microsoft Academic Search

The water cycle in the Martian atmosphere is influenced by exchange with the subsurface, condensation on the surface, mixing between the boundary layer and the free atmosphere, large-scale horizontal mixing of air masses and precipitation as water ice particles. We have installed a water cycle model with microphysics processes into the Martian Weather Research and Forecast (WRF) model. It treats

A. Inada; M. I. Richardson; M. A. Mischna; C. E. Newman; A. D. Toigo; A. R. Vasavada

2005-01-01

439

Building robust models to forecast the induced seismicity related to geothermal reservoir enhancement  

NASA Astrophysics Data System (ADS)

We test the Epistemic Type Aftershock Sequence, (ETAS) and the Reasenberg and Jones (R&J) models, which are the commonly used models for aftershock forecasting, for the induced seismicity sequence of the Enhanced Geothermal System (EGS) in Basel, in a pseudo-prospective manner. In addition to these two statistical models, we introduce the model of Shapiro et al. (2010) for forecasting induced seismicity due to EGS in a pseudo-prospective modeling approach. While the ETAS and the R&J models are statistical models, the model of Shapiro et al. (2010) is physics based method that takes into account the flow-rate and the seismogenic index that characterizes the level of seismic activity expected from injecting fluid into rock. We aim to define a weighted logic tree approach as input for induced seismicity probabilistic seismic hazard assessment. High performance forecast models defined in a weighted logic tree approach and then converted into time dependent probabilistic seismic hazard can feed probabilistic alarm systems for EGS experiments. We forecast the seismicity rates of the next six hours based on these three model classes using different modeling and updating strategies. We quantitatively test the performances of the models and define a combined model constructed using Akaike weights. We show that such performance testing can be used as an indication for logic tree weighting. We also evaluate the performances of different models in forecasting a certain magnitude/magnitude range (for instance number of events with M?2 that are of more concern). In addition, we perform a test on how well we can forecast during and post injection seismicity, with the very first coming data (first day or days). This initial testing with recordings of limited time can reveal the suitability of a site for full reservoir stimulation. Robust forecast models can lead us to an early operation of the traffic light system where a decision on continuing/slowing-down/stopping of fluid injection can be feasible before possible larger magnitude events occur.

Mena Cabrera, B.; Wiemer, S.; Bachmann, C. E.

2012-04-01

440

Sparse High Dimensional Models in Economics  

PubMed Central

This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed.

Fan, Jianqing; Lv, Jinchi; Qi, Lei

2010-01-01