 Proposing a novel interval-valued electricity demand forecasting approach.  BEMD and SVR are integrated for interval forecasting of electricity demand.  The EMD-based modeling framework are extended to deal with interval forecasting  BEMD is used to decompose both the lower and upper bounds electricity demand series.  The proposed modeling framework is justified with real world data sets....

Tourism in Greece plays a major role in the country’s economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An A...

In this study, statistical models were developed to forecast truck VMT growth of four facility categories at the county and statewide levels. These models incorporate both socioeconomic and transportation system supply variables. Different model specifications were tested and evaluated in terms of statistical and forecasting validity. A selected set of models was used to forecast truck VMT for ...

AUTOMATION OF ENERGY DEMAND FORECASTING Sanzad Siddique, B.S. Marquette University, 2013 Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Furth...

The intermittent demand forecasting problem involves the forecasting of demand series that are characterized by the time between demands being significantly larger than the unit of time used for the forecast period. This causes the time series associated with the demand to have a large percentage of periods for which there are no demands. These types of series are often found in spare parts inv...

abstract autoregressive integrated moving average (arima) has been one of the widely used linear models in time series forecasting during the past three decades. recent studies revealed the superiority of artificial neural network (ann) over traditional linear models in forecasting. but neither arima nor anns can be adequate in modeling and forecasting time series since the first model cannot d...

Abstract: Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting mod...

Combining forecasts from different models has shown to perform better than single forecasts in most times series. In this paper new techniques for combining a large number of forecasting models in order to achieve better forecasting performance are introduced. This class of new techniques for combining is based on using consistent experts for forecasting.

In this paper, we propose a new residual analysis method using Fourier series transform into fuzzy time series model for improving the forecasting performance. This hybrid model takes advantage of the high predictable power of fuzzy time series model and Fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...