科研之友
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摘要
In line with the decomposition-and-reconstitution principle, the empirical mode decomposition (EMD)-based modeling framework has been a widely used alternative for non-linear, non-stationary time series prediction to decompose an original time series into different sub-series that can be identified, separately predicted, and then recombined for aggregate forecasting. However, in many cases, recombination has been found to adversely affect prediction accuracy. To address this problem, this study incorporates a feed forward neural network (FNN) into the EMD-based forecasting framework and brings forward the weighted recombination strategy to allow for one step ahead forward prediction. To justify and compare the effectiveness of the proposed model, four non-linear, non-stationary data series are applied and benchmarked using four well-established prediction model recombination methods. The results show that the proposed weighted EMD-based forecasting model observably improves forecast validity. This approach also has great promise for intricate and noise disturbed irregular and highly volatile time series predictions.
