Forecast Time Horizon Research Articles

Forecasting Load Using Continuous-time Fractionally Auto-Regressive Integrated Moving-Average Models with Discrete Data

In a deregulated economy, it is important to forecast load on daily. It has many applications including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. A large variety of mathematical methods have been developed for load forecasting. In this paper, we used a continuous-time autoregressive fractionally integrated moving average (CRAFIMA) model, which is defined as the stationary solution of a stochastic differential equation driven by a standard fractional Brownian motion. Like the discrete-time ARFIMA model, the CRAFIMA model is useful for studying time series with short memory, long memory and anti-persistence. Optimization issues including hyper-parameter selection and feature selection are also discussed. The study found that all three coefficients of the Hurst parameters are positive which is an advantage of using CRAFIMA algorithm. Based on this observations we conclude that self-similarity of the deseasonalized load Y(t) plays an important role in load prediction problems. The study also shows that the higher the Hurst exponent H , the bigger the range of short-term forecasting time horizons where the CRAFIMA model yields more accurate results than fitted ARIMA and f ARIMA models.

Currency forecast errors and carry trades at times of low interest rates: Evidence from survey data on the yen/dollar exchange rate

Using survey expectations data and a variant of the uncovered interest rate parity (UIRP), this paper evaluates the relationship between interest rates and investors' forecast errors about the yen/dollar exchange rate. This study therefore is related to the forward premium puzzle and the currency carry trade strategy. Using relatively short (i.e., 1 to 6 month-ahead) forecast data, we obtain the following results. First, with the same forecast horizon, exchange rate forecasts are homogeneous among different industry types, but within the same industry, exchange rate forecasts differ if the forecast time horizon is different. Second, while forecast errors are found to be partly driven by interest rate spreads, evidence against the UIRP is provided. Consistent with this finding, our coefficients on interest rate spreads obtained from nonlinear models provide indirect evidence of the yen carry trade. In particular, unlike theoretical predictions, the carry trade is found to be active during financial turmoil and when there is a clear indication that the interest rate will be low in the future.

Http://www.growingscience.com/msl/Vol5/msl_2015_43.pdf

Article history: Received January 25, 2015 Received in revised format 28 March 2015 Accepted 2 May 2015 Available online May 3 2015 This paper presents an empirical investigation to study the effects of quality of earnings forecasts in predicting stock returns on 121 selected firms traded on Tehran Stock Exchange over the period 2009-2013. The study investigates the effects of three year means of earnings forecast accuracy on investors’ investment decisions in terms of volume and time horizon, i.e. short term and long term investment sentiment. Using some regression analysis, the study has determined a positive and meaningful relationship between the quality of earnings forecast and investment time horizon. However, the study did not find any meaningful relationship between earnings forecast accuracy and volume of investment. Growing Science Ltd. All rights reserved. 5 © 201

Evaluation of forest management strategies based on Triad zoning

Triad forest management was analyzed for a New Brunswick Crown License. Fifteen forest value indicators were used to describe social, economic, and environmental outcomes from forecast Triad scenarios, including 36 scenarios where reserves and intensively managed area varied in 5% increments from 10% to 35%. Some indicators were most sensitive to intensive area (e.g., silviculture cost), other to reserve area (e.g., area containing large snags), and still others to extensive area (e.g., average harvest levels). Some indicators averaged arithmetically, and could be kept constant if increases in reserves were accompanied by equal increases in intensive area. Such averaging for timber supply is often a selling point made by Triad advocates. Indeed, many different scenarios generated the same annual harvest when averaged over the 100-year forecast time horizon; however, immediate reductions in operable timber inventory resulting from reserve increases caused short-term harvest reductions, while future gains in yield from intensive area increases caused long-term harvest increases. This timing offset between losses and gains of operable volume, and its effect on harvest timing, may be impediments to Triad implementation in jurisdictions where timber supply is fully utilized. This analysis presents methods and results that may be of value to forest managers contemplating implementation of Triad zoning.

Does Money Supply Growth Contain Predictive Power for Stock Returns? Evidence and Explanation

We examine the nature of predictive power of money supply growth for stock returns. An understanding of which would be useful for market practitioners and policy makers given the current lack of consensus. In addition, knowledge of this relationship can aid our understanding of the risk and cash flow channels of stock price movement. Using monthly data and considering a range of alternative predictor variables, predictive and forecast time horizons, empirical methodologies and measures of stock returns, we find that money supply growth has a negative predictive relation with stock returns. Rolling window forecasts provide confirmatory out-of-sample evidence for the significance of money supply growth as a predictor variable. The use of both returns and dividend growth predictive regressions, as well as a VAR analysis and predictive regressions for size and book-to-market portfolios suggest that relationship operates through a risk channel. Higher money supply leads to improving economic conditions and lower (required) returns.

Making the same mistake all over again: CEO overconfidence and corporate resistance to corrective feedback

Firms often make mistakes, from simple manufacturing overruns all the way to catastrophic blunders. However, there is considerable heterogeneity in the nature of corporate responses when faced with evidence that an error has taken place, and, therefore, in the likelihood that such errors will reoccur in the future. In this paper, we explore an important but understudied influence on firms' responses to corrective feedback—a CEO's level of overconfidence. Using multiple distinct measures of overconfidence and the empirical context of voluntary corporate earnings forecasts, we find strong, robust evidence that firms led by overconfident CEOs are less responsive to corrective feedback in improving management forecast accuracy. We further show that this relationship is moderated by prior forecast error valence, time horizon, and managerial discretion

Wave energy forecasting using artificial neural networks in the Caspian Sea

Providing energy without unfavourable impact on the environment is an important issue that is considered by societies. This paper focuses on forecasting the wave energy over horizons of 1–12 h, in the southern part of the Caspian Sea. For this purpose, an artificial neural network was used to obtain the wave energy flux using two different methods. First, the components of wave energy flux, including the significant wave height and peak wave period were predicted separately and the wave energy flux was calculated by combining them; and second, the wave energy flux was forecast directly. The results showed that the prediction of components separately yielded more accurate results. It was found that the longer the forecasting time horizon, the less accurate was the prediction. This is because in large time horizons, the previous wave characteristics have little influence on the wave energy flux. The forecast wave energy flux in both methods correlated well with observed data in short horizons.

Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods

By means of wavelet transform, an ARIMA time series can be split into different frequency components. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own characteristics. Eventually, we come to the conclusion that using wavelets does improve forecasting quality, especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.

Hierarchical neurofuzzy model for real-time flood forecasting

The current study employs a hierarchical adaptive network-based fuzzy inference system for flood forecasting by developing a rainfall–runoff model for the Narmada basin in India. A hybrid learning algorithm, which combines the least-square method and a back propagation algorithm, is used to identify the parameters of the network. A subtractive clustering algorithm is used for input space partitioning in the fuzzy and neurofuzzy models. The model architectures are trained incrementally each time step and different models are developed to predict one-step and multi-step ahead forecasts. The number of input variables is determined using a standard statistical method. An artificial neural network (ANN) model which uses an Levenberg–Marquardt (LM) backpropagation training algorithm has been developed for the same basin. The results of this study indicate that the hierarchical neurofuzzy model performs better compared to an ANN and the standard fuzzy model in estimating hydrograph characteristics, especially at longer forecast time horizons.

Mixture Models for Improved Earthquake Forecasting with Short-to-Medium Time Horizons

Abstract Optimal mixtures of three space–time–magnitude earthquake likelihood models are found for forecasting earthquakes with magnitudes of 5.0 and greater in the New Zealand and California catalogs, with forecasting time horizons ranging from 0 to 3000 days. The models are the Epidemic‐Type Aftershock (ETAS) short‐term clustering model, the Every Earthquake a Precursor According to Scale (EEPAS) medium‐term clustering model, and the Proximity to Past Earthquakes (PPE) quasi‐time‐invariant smoothed seismicity model. The ETAS model is by far the most informative of these models for short time horizons of a few days, but even with a zero time horizon, an optimal mixture of the three models, here called the Janus model, outperforms it with an information gain per earthquake (IGPE) of about 0.1. For time horizons of 10–3000 days, the Janus model outperforms the most informative of its component models with IGPEs ranging from 0.2 to 0.5. As the time horizon lengthens beyond six months in New Zealand and two years in California, the EEPAS model becomes the most informative of the individual models and the major component of the optimal mixture. Changes in the Janus model parameters with the forecasting time horizon reveal features of time‐and‐area scaling of precursory seismicity. The results suggest that both cascades of triggering and the precursory scale increase phenomenon contribute to earthquake predictability and that these contributions are largely independent.

Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space

Abstract. An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This paper evaluates the generalization ability of a simplification scheme of a 800-member HEPS formed by the combination of 16 lumped rainfall-runoff models with the 50 perturbed members from the European Centre for Medium-range Weather Forecasts (ECMWF) EPS. Tests are made at two levels. At the local level, the transferability of the 9th day hydrological member selection for the other 8 forecast horizons exhibits an 82% success rate. The other evaluation is made at the regional or cluster level, the transferability from one catchment to another from within a cluster of watersheds also leads to a good performance (85% success rate), especially for forecast time horizons above 3 days and when the basins that formed the cluster presented themselves a good performance on an individual basis. Diversity, defined as hydrological model complementarity addressing different aspects of a forecast, was identified as the critical factor for proper selection applications.

Management Forecasts Subsequent to Financial Restatement Disclosures

We examine the impact of financial restatements on managers’ subsequent forecast behavior. We find that managers reduce their propensity to issue forecasts, especially good news forecasts, after restatements. The quality of forecasts improves after restatements (i.e. forecasts have smaller errors and less optimistic bias). We find that managers are more likely to provide less precise forecasts following restatements (i.e. fewer point forecasts, more range forecasts). Managers’ propensity to issue forecasts following restatements varies with forecast time horizons. Specifically, managers reduce their propensity to issue long-term forecasts, but not to issue earnings warnings or short-term bad news forecasts. Overall our results suggest that managers are more concerned about increased personal risk following restatements, than they are interested in reducing information asymmetry between themselves and investors.

Global Oil Production: Forecasts and Methodologies

A range of forecasts of global oil production made between 1956 and the present day are listed. For the majority of these the methodology used to generate the forecast is described. The paper distinguishes between three types of forecast: group 1—quantitative analyses which predict that global oil production will reach a resource-limited peak in the near term, and certainly before the year 2020; group 2—forecasts that use quantitative methods, but which see no production peak within the forecast's time horizon (typically 2020 or 2030); group 3—nonquantitative analyses that rule out a resource-limited oil peak within the foreseeable future. The paper analyses these forecast types and suggests that group 1 forecasts are the most realistic.

Environmental variability and fisheries: what can models do?

This review is based on 58 climate-fisheries models published over the last 28 years that describe the impacts of fishery pressure and environmental variability on populations and ecosystems and include basic principles of population dynamics. It points out that the incorporation of environmental factors in fishery models has already been done and is of great importance for future models used in the assessment of marine resources. The work is guided by the questions to what extent have these models a) enhanced our understanding of the interrelationships between the environment, the fishery and the state of the exploited resources and b) helped to improve the prediction of population dynamics and the assessment of marine resources. For each of the six most commonly used model categories a case study is critically analyzed. The problems of “breaking relationships” between environmental factors and the biological response used in models, the trade-off between model complexity (realism) and simplicity (data availability) and the potential of multivariate climate indices for forecasting ecosystem states and for use as proxies for combined models are discussed, as are novel non-linear and spatially explicit modeling approaches. Approaches differ in terms of model complexity, use of linear or non-linear equations, number of parameters, forecast time horizon and type of resource modelled. A majority of the models were constructed for fish and invertebrate stocks of the northeast Pacific and the epicontinental seas of the Atlantic, reflecting the advancement of fisheries science in these regions. New, in parts highly complex models and sophisticated approaches were identified. The reviewed studies demonstrate that the performance of fished stocks can better be described if environmental or climatic variability is incorporated into the fisheries models. We conclude that due to the already available knowledge, the greatly enhanced computer power, new methods and recent findings of large-scale climatic/oceanographic cycles, fisheries modeling should progress greatly in the coming years.

Effect of inflow forecast accuracy and operating time horizon in optimizing irrigation releases

In this study, application of Genetic Algorithms (GA) is demonstrated to optimize reservoir release policies to meet irrigation demand and storage requirements. As it is commonly recognized that accuracy of inflow forecast and operating time horizon affects the optimal policies, a trial-and-error approach is suggested to identify the appropriate trade-off between forecast accuracy and operating horizon. The flexibility offered by GA to set up and evaluate objective functions is exploited towards this end. The results are also compared with Linear Programming (LP) model. It is concluded that forecasts models of high accuracy are desirable, particularly when the system is to be operated for periods of high demand. In such cases, the optimization with longer time horizon ensures achievement of the objective more uniformly over the period of operation. The performance of GA is found to be better than LP, when forecast model of higher accuracy and longer period of operating horizon are considered for optimization.

An analysis of a panel of Spanish GDP forecasts

This article undertakes an analysis of the forecasts included in the Panel of Spanish Forecasts in order to highlight the fact that the predictions made and the errors committed by the entities participating on this panel contain information that is particularly useful in analysing the evolution of the Spanish economy. Here, a study is undertaken of the GDP growth forecasts for the Spanish economy for the period 2000 to 2002 made in distinct forecast time horizons. Specifically, it analyses whether the forecasts are optimistic or pessimistic and whether new information concerning the variable being predicted is used efficiently in revising earlier forecasts.

A Mixed Integer Stochastic Optimization Model for Settlement Risk in Retail Electric Power Markets

Recent changes in the U.S. electric power markets have contributed to volatility in hourly prices and loads. In this paper we consider the position of the electric power retailer who typically contracts with suppliers and end-users and must provide future load requirements to the suppliers. As part of this energy supply chain, the retailer is faced with great uncertainty in both market prices as well as end-user loads. Based on actual data for the PJM market covering Pennsylvania, New Jersey, and Maryland, we develop a probabilistic optimization model to optimize the net profits for the retailer for a forecast time horizon (typically one or more hours) given the cumulative performance in previous time periods (hours). The resulting model is formulated as a mixed integer linear program with binary variables due to the disjunctive nature of certain forward load estimation “bandwidth” tolerance constraints. In addition, we also provide an existence result to this optimization model. Lastly, we present a numerical example of the optimization model to validate its workings and provide some insight into model sensitivities.

An exploratory Investigation of new product forecasting practices

To guide new product forecasting efforts, the following study offers preliminary data on new product forecasting practices during the commercialization stage (prelaunch and launch stage). Data on department responsibility for and involvement in the new product forecasting process, technique usage, forecast accuracy, and forecast time horizon across different types of new products are reported. Comparisons of new product forecasting practices for consumer firms versus industrial firms are also reported.Overall, study results show that the marketing department is predominantly responsible for the new product forecasting effort, there is a preference to employ judgmental forecasting techniques, forecast accuracy is 58% on average across the different types of new products, and two to four forecasting techniques are typically employed during the new product forecasting effort. Compared to consumer firms, industrial firms appear to have longer forecast time horizons and rely more on the sales force for new product forecasting. Additional analyses show that there does not appear to be a general relationship between a particular department's involvement and higher forecast accuracy or greater satisfaction, nor does it appear that use of a particular technique relates to higher forecast accuracy and greater satisfaction. Countering previous research findings, the number of forecasting techniques employed also does not appear to correlate to higher forecasting accuracy or greater satisfaction. Managerial and research implications are discussed.

An exploratory investigation of new product forecasting practices

To guide new product forecasting efforts, the following study offers preliminary data on new product forecasting practices during the commercialization stage (prelaunch and launch stage). Data on department responsibility for and involvement in the new product forecasting process, technique usage, forecast accuracy, and forecast time horizon across different types of new products are reported. Comparisons of new product forecasting practices for consumer firms versus industrial firms are also reported. Overall, study results show that the marketing department is predominantly responsible for the new product forecasting effort, there is a preference to employ judgmental forecasting techniques, forecast accuracy is 58% on average across the different types of new products, and two to four forecasting techniques are typically employed during the new product forecasting effort. Compared to consumer firms, industrial firms appear to have longer forecast time horizons and rely more on the sales force for new product forecasting. Additional analyses show that there does not appear to be a general relationship between a particular department’s involvement and higher forecast accuracy or greater satisfaction, nor does it appear that use of a particular technique relates to higher forecast accuracy and greater satisfaction. Countering previous research findings, the number of forecasting techniques employed also does not appear to correlate to higher forecasting accuracy or greater satisfaction. Managerial and research implications are discussed.

Forecasting Models of Retail Rents

The authors model retail rents in the United Kingdom with use of vector-autoregressive and time-series models. Two retail rent series are used, compiled by LaSalle Investment Management and CB Hillier Parker, and the emphasis is on forecasting. The results suggest that the use of the vector-autoregression and time-series models in this paper can pick up important features of the data that are useful for forecasting purposes. The relative forecasting performance of the models appears to be subject to the length of the forecast time-horizon. The results also show that the variables which were appropriate for inclusion in the vector-autoregression systems differ between the two rent series, suggesting that the structure of optimal models for predicting retail rents could be specific to the rent index used. Ex ante forecasts from our time-series suggest that both LaSalle Investment Management and CB Hillier Parker real retail rents will exhibit an annual growth rate above their long-term mean.