Time Series Forecasting Based on Empirical Mode Decomposition and the Varying-Coefficient DBN-AR Model

Abstract

In an attempt to improve the prediction accuracy for a time series, a new one-step-ahead hybrid model is proposed in this paper that combines empirical mode decomposition (EMD) with the DBN-AR model and back propagation (BP). The proposed approach first uses EMD, which can be used to decompose the complicated original time series data into several intrinsic mode functions (IMFs) and a residue. The IMF components and residue are then modelled and forecasted using the DBN-AR model. Finally, the predicted results for all IMFs and a residue are combined by a BP neural network to obtain an aggregated output for the time series data. To evaluate the performance of the proposed hybrid model, Beijing PM2.5 level time series data and the weekly rates of British Pound/US dollar (GBP/USD) exchange rate data are used as an illustrative example. Experimental results demonstrate the attractiveness of the proposed hybrid model based on both the prediction accuracy and efficiency compared with other methods.