Back propagation neural network with adaptive differential evolution algorithm for time series forecasting

• We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.
• ADE is used to search for global initial connection weights and thresholds of BPNN.
• The proposed ADE–BPNN is effective for improving forecasting accuracy.

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.