A linear hybrid methodology for improving accuracy of time series forecasting
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Modeling and forecasting of time series data are integral parts of many scientific and engineering applications. Increasing precision of the performed forecasts is highly desirable but a difficult task, facing a number of mathematical as well as decision-making challenges. This paper presents a novel approach for linearly combining multiple models in order to improve time series forecasting accuracy. Our approach is based on the assumption that each future observation of a time series is a linear combination of the arithmetic mean and median of the forecasts from all participated models together with a random noise. The proposed ensemble is constructed with five different forecasting models and is tested on six real-world time series. Obtained results demonstrate that the forecasting accuracies are significantly improved through our combination mechanism. A nonparametric statistical analysis is also carried out to show the superior forecasting performances of the proposed ensemble scheme over the individual models as well as a number of other forecast combination techniques.
KeywordsTime series Forecast combination Box-Jenkins models Artificial neural networks Elman networks Support vector machines
The authors are thankful to the reviewers for their constructive suggestions which significantly facilitated the quality improvement of this paper. In addition, the first author likes to express his profound gratitude to the Council of Scientific and Industrial Research (CSIR), India, for the obtained financial support in performing this research work.
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