A linear hybrid methodology for improving accuracy of time series forecasting

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.