A neural network based linear ensemble framework for time series forecasting

Combining time series forecasts from several models is a fruitful alternative to using only a single individual model. In the literature, it has been widely documented that a combined forecast improves the overall accuracy to a great extent and is often better than the forecast of each component model. The accuracy of a linear combination of forecasts primarily depends on the associated combining weights. Despite extensive research in this direction, finding out the most appropriate weights is still very challenging. This paper proposes a linear combination method for time series forecasting that determines the combining weights through a novel neural network structure. The designed neural network successively recognizes the weight patterns of the constituent models from their past forecasting records and then predicts the desired set of the combining weights. Empirical results from eight real-world time series show that our approach provides significantly better forecasting accuracies than the component models and other well recognized linear combination schemes. These findings are also verified through ranking methods and a non-parametric statistical test.