Sunspots Time-Series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network

Abstract EN

The sunspot numbers are the major target which describes the solar activity level.

Long-term prediction of sunspot activity is of great importance for aerospace, communication, disaster prevention, and so on.

To improve the prediction accuracy of sunspot time series, the prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and wavelet neural network (WNN) is proposed.

First, the sunspot time series are decomposed by CEEMD to obtain a set of intrinsic modal functions (IMFs).

Then, the IMFs and residuals are reconstructed to obtain the training samples and the prediction samples, and these samples are trained and predicted by WNN.

Finally, the reconstructed IMFs and residuals are the final prediction results.

Five kinds of prediction models are compared, which are BP neural network prediction model, WNN prediction model, empirical mode decomposition and WNN hybrid prediction model, ensemble empirical mode decomposition and WNN hybrid prediction model, and the proposed method in this paper.

The same sunspot time series are predicted with five kinds of prediction models.

The experimental results show that the proposed model has better prediction accuracy and smaller error.