Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 42, Issue 2, Pages 855-863Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2014.08.018
Keywords
Time series forecasting; Back propagation neural network; Differential evolution algorithm
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Funding
- National Natural Science Foundation of China [71371080, 71373093]
- Humanities and Social Sciences Foundation of Chinese Ministry of Education [11YJC630275]
- Fundamental Research Funds for the Central Universities [HUST: 2014QN201]
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The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE-BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE-BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models. (C) 2014 Elsevier Ltd. All rights reserved.
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