文献类型：期刊文献

中文题名：Forecasting solar cycles using the time-series dense encoder deep learning model

作者：Cui Zhao[1];Shangbin Yang[2,3];Jianguo Liu[1];Shiyuan Liu[1]

第一作者：Cui Zhao

机构：[1]College of Applied Science and Technology,Beijing Union University,Beijing 102200,China;[2]State Key Laboratory of Solar Activity and Space Weather,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China;[3]University of Chinese Academy of Sciences,Beijing 100049,China

第一机构：北京联合大学应用科技学院

年份：2026

卷号：3

期号：1

起止页码：43-54

中文期刊名：Astronomical Techniques and Instruments

外文期刊名：天文技术与仪器(英文)

基金：supported by the Academic Research Projects of Beijing Union University(ZK20202204);the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052);the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102);the Chinese Meridian Project(CMP).

语种：英文

中文关键词：Solar cycle;Forecasting;TiDE;Deep learning

摘要：The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.