Two Stage Prediction Model of Sunspots Monthly Value Based on CEEMDAN and Particle Swarm Optimization ELM

Sunspot number is the most basic parameter to describe the level of solar activity. The accurate prediction of sunspot number can reflect the electromagnetic disturbance level of the electromagnetic layer, ionosphere and the middle and high layers in the future in advance, so as to provide important reference information for navigation, positioning, communication and the prediction of orbital attenuation of LEO satellites. Aiming at the characteristics of sunspot time series such as non-stationary, chaotic and difficult to predict, this paper proposed a two-stage combined prediction model based on complete Ensemble Empirical mode decomposition with adaptive noise (CEEMDAN), particle swarm optimization (PSO) and extreme learning machine(ELM) network. In the first stage of prediction: firstly, the original sunspot monthly mean series is decomposed by CEEMDAN to reduce the non-linearity and non-stationary of the original series. Then, an ELM prediction model is established for the sub-sequences decomposed by CEEMDAN, and the input layer dimension, hidden layer dimension, input layer weight and hidden layer bias of ELM are optimized by PSO algorithm. Finally, the prediction results of the first stage are obtained by superimposing the prediction results of each sub-sequence. The second stage is the error self-correction stage. Firstly, the prediction error sequence of the first stage is obtained. Then, the CEEMDAN-PSO-ELM prediction model is used to self-correct the prediction error of the first stage. Finally, the prediction results of the first stage and the self-correction results of the second stage are superimposed to obtain the final prediction value of the monthly sunspot number. In this paper, CEEMDAN is used to reduce the non-linearity and non-stationary of the sunspot series, and PSO is used to determine the best parameters of ELM network, and the useful information in the prediction error is fully considered, which effectively improves the prediction accuracy of sunspot monthly mean series. The prediction experiment is carried out by using the measured sunspot monthly mean series, and the proposed model is compared with wavelet neural network (WNN), back propagation neural network (BPNN), ELM, CEEMDAN-ELM and CEEMDAN-PSO-ELM. The experimental results show that the prediction accuracy of the proposed two-stage prediction model has been significantly improved, and has better prediction stability.

Automated summary

This paper proposes a two-stage combined prediction model based on CEEMDAN, PSO, and ELM for accurately predicting the monthly mean sunspot number. By decomposing the series, modeling the prediction, and self-correction, the model significantly improves the prediction accuracy and stability of sunspot monthly mean series.