Performance evaluation of weights selection schemes for linear combination of multiple forecasts

摘要

Time series modeling and forecasting are essential in many domains of science and engineering. Extensive works in literature suggest that combining outputs of different forecasting methods substantially increases the overall accuracies as well as reduces the risk of model selection. The most popular method of forecasts combination is the weighted averaging of the constituent forecasts. The effectiveness of this method solely depends on appropriate selection of the combining weights. In this paper, we comprehensively evaluate a wide variety of benchmark weights selection techniques for linear combination of multiple forecasts in terms of their prediction accuracies. Nine real-world time series from different domains and five individual forecasting methods are used in our empirical work. A robust scheme is also suggested for fairly ranking the combination methods on the basis of their forecasting performances. Our study precisely demonstrates the relative strengths and weaknesses of various benchmark linear combination techniques which evidently can be of much practical importance.