A Novel Weighted Ensemble Technique for Time Series Forecasting
Improvement of time series forecasting accuracy is an active research area having significant importance in many practical domains. Extensive works in literature suggest that substantial enhancement in accuracies can be achieved by combining forecasts from different models. However, forecasts combination is a difficult as well as a challenging task due to various reasons and often simple linear methods are used for this purpose. In this paper, we propose a nonlinear weighted ensemble mechanism for combining forecasts from multiple time series models. The proposed method considers the individual forecasts as well as the correlations in pairs of forecasts for creating the ensemble. A successive validation approach is formulated to determine the appropriate combination weights. Three popular models are used to build up the ensemble which is then empirically tested on three real-world time series. Obtained forecasting results, measured through three well-known error statistics demonstrate that the proposed ensemble method provides significantly better accuracies than each individual model.
KeywordsTime Series Forecasting Ensemble Technique Box-Jenkins Models Artificial Neural Networks Elman Networks
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