A linear hybrid methodology for improving accuracy of time series forecasting

作者：Adhikari Ratnadip^*; Agrawal R K

来源：Neural Computing & Applications, 2014, 25(2): 269-281.

DOI：10.1007/s00521-013-1480-1

• Time series
• Forecast combination
• Box-Jenkins models
• Artificial neural networks
• Elman networks
• Support vector machines

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.

• 出版日期2014-8