Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (12): 60-70.doi: 10.11896/jsjkx.241100011

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multiscale Sunspot Number Forecasting Based on Decomposition and Integration

ZHAO Yuxuan1, YU Dingfeng2,3, LI Dongxue1, XU Yidong1, LI Beiming1   

  1. 1 Yantai Research Institute of Harbin Engineering University, Yantai, Shandong 264000, China
    2 Hubei Key Laboratory of Marine Electromagnetic Detection and Control, Wuhan 430064, China
    3 Wuhan Second Ship Design and Research Institute, Wuhan 430064, China
  • Received:2024-11-04 Revised:2025-02-11 Online:2025-12-15 Published:2025-12-09
  • About author:ZHAO Yuxuan,born in 2002,postgra-duate,is a member of CCF(No.V1529G).His main research interest is time series analysis and prediction.
    YU Dingfeng,born in 1986,Ph.D,senior engineer.His main research interests include electromagnetic theory and its application and electromagnetic field signal processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(52101383) and Basic Research Funds of Central Universities (3072024JJ2702,3072023LH0802).

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Abstract: Solar activity exerts a direct influence on the heliosphere environment and life on earth.SN represents one of the most crucial and frequently predicted indices of solar activity.Enhancing the accuracy of SN predictions can provide more reliable data support for climate models,which is of great significance for understanding the solar activity cycle.This paper proposes a multi-scale SN sequence prediction model,which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),a hybrid neural network and an attention mechanism.The method employs three distinct datasets:the Daily Total SN from 1818 to 2024,the Monthly Mean Total SN from 1749 to 2024,and the Yearly Mean SN from 1700 to 2023.Given the non-stationary,non-Gaussian and non-linear nature of the SN time series,the CEEMDAN method is initially employed to decompose the components of solar activity changes on various time scales into a number of sub-series with different frequencies.These sub-series are then combined with the original series as a reinforced feature set,thereby enhancing the model’s ability to characterize the changes in solar activity.TCNs are then employed as the primary means of feature extraction,followed by the incorporation of BiLSTM to capture the long-term dependence of the time series.Additionally,Attention mechanisms are introduced to dynamically identify and weight the key temporal features in the sequence.Ablation experiments are conducted on three datasets concurrently,and the results demonstrate a notable synergy between the modules of the proposed model.A comparison of the existing models on this basis reveals that the prediction accuracy of each dataset has been enhanced.The proposed model is employed to predict SN,resulting in the acquisition of three distinct frequencies of yearly,monthly,and daily prediction outcomes.These are subsequently integrated as multi-timescale features to generate the final prediction results.The results indicate that solar activity has shown a significant increasing trend in 2025 and is expected to reach the peak of Solar Cycle 25 within this year,with an estimated annual mean SN of 233.9.

Key words: Prediction of sunspot number, Time series decomposition, Temporal convolutional network, Bidirectional long short-term memory network, Attention mechanisms, Solar cycle

CLC Number: 

  • TP391
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