Stacked 1D Convolutional LSTM (sConvLSTM1D) Model for Effective Prediction of Sunspot Time Series

被引：0
作者：
Abhijeet Kumar
Vipin Kumar
机构：
[1] Mahatma Gandhi Central University,Department of Computer Science and Information Technology
来源：
Solar Physics | 2023年 / 298卷
关键词：
Sunspot number; Solar cycle; Prediction; Time series; Deep learning; Convolutional; LSTM; Hybrid model; Stack model;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A multi-layer, deep-learning (DL) architecture consisting of stacked Convolutional Long Short Term Memory (sConvLSTM1D) layers is proposed to forecast the sunspot number (SSN) more effectively. The proposed model with optimized hyper-parameters performs efficiently on four kinds of sunspot data with different frequencies of time that are yearly, monthly, daily, and 13-month smoothed provided by the World Data Center-Sunspot Index and Long Term Solar Observation (WDC-SILSO), the Royal Observatory of Belgium (SILSO World Data Center). The model was contrasted with other traditional DL models on different performance metrics, namely root-mean-square error (RMSE), mean-absolute error (MAE), mean-absolute-percentage error (MAPE), and mean-absolute-scaled error (MASE). A non-parametric statistical test has also been carried out to confirm the model’s effectiveness. The prediction of the highest yearly mean of total sunspot number (SSN) in Solar Cycle 25 (SC25) has also been performed. The proposed sConvLSTM1D model suggests that the solar cycle exhibits the characteristics of a weak cycle. However, it is anticipated to be stronger than the preceding Solar Cycle 24 (SC24). The year of peak sunspot number will be 2024, as per the prediction, with the peak value of yearly mean sunspot number as 140.84, which is 24.3% higher than the peak value of the yearly mean of total sunspot number, which was 113.3 in the Solar Cycle 24 in the year 2014.
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