论文标题
对黑子数量预测的非深度学习，深度学习和集合学习方法的比较研究
A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
论文作者
论文摘要
太阳能活动对人类活动和健康有重大影响。太阳能活动的最常用量度之一是黑子数。本文比较了三个重要的非深度学习模型，四个流行的深度学习模型及其在预测黑子数字中的五个合奏模型。特别是，我们提出了一个称为XGBoost-DL的集合模型，该模型使用XGBoost用作两级非线性集合方法来结合深度学习模型。我们的XGBoost-DL在比较中取得了最佳预测性能（RMSE = 25.70和MAE = 19.82），表现优于最佳的非深度学习模型Sarima（RMSE = 54.11 = 54.11和MAE = 45.51），是最佳的深度学习模型，最佳的深度学习模型（RMSE = 29.90 and Mae = 29.90 and Mae = 22.35）和48.90和Mae 3和48.35和48.35）和48.35 and forec。 MAE = 38.45）。我们的XGBOOST-DL预测2025年5月的太阳周期25周期和2035年11月26日的133.47峰值的峰值数量为164.62，太阳能周期为26个周期，类似于2024年10月10日的NASA的137.7，以及2034年12月2日的137.7 https://github.com/yd1008/ts_ensemble_sunspot。
Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. In particular, we propose an ensemble model called XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models. Our XGBoost-DL achieves the best forecasting performance (RMSE = 25.70 and MAE = 19.82) in the comparison, outperforming the best non-deep learning model SARIMA (RMSE = 54.11 and MAE = 45.51), the best deep learning model Informer (RMSE = 29.90 and MAE = 22.35) and the NASA's forecast (RMSE = 48.38 and MAE = 38.45). Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034. An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://github.com/yd1008/ts_ensemble_sunspot.
