论文标题
使用深神经网络的预测太阳能周期25
Forecasting Solar Cycle 25 using Deep Neural Networks
论文作者
论文摘要
随着机器学习领域的最新进展，将深度神经网络用于时间序列预测变得更加普遍。太阳周期的准周期性质使其成为应用时间序列预测方法的良好候选者。我们使用1749年至2019年的WAVENET和LSTM神经网络的组合，使用1874年至2019年的时间序列数据来预测黑子数，以及与我们的最佳模型相比，还使用1874年至2019年的时间序列数据。我们的分析表明，Wavenet和LSTM模型能够更好地捕获整体趋势，并了解时间序列数据中固有的长期和短期依赖关系。使用这种方法，我们预测太阳周期25的每月平均数据为11年。我们的预测表明，即将到来的太阳能周期25的最大黑子数量约为106 $ \ pm $ 19.75 $ 19.75，最大的太阳点左右左右左右左右左右面积约为1771 $ \ \ pm $ 381.17。这表明周期将比太阳周期24稍弱。
With recent advances in the field of machine learning, the use of deep neural networks for time series forecasting has become more prevalent. The quasi-periodic nature of the solar cycle makes it a good candidate for applying time series forecasting methods. We employ a combination of WaveNet and LSTM neural networks to forecast the sunspot number using the years 1749 to 2019 and total sunspot area using the years 1874 to 2019 time series data for the upcoming Solar Cycle 25. Three other models involving the use of LSTMs and 1D ConvNets are also compared with our best model. Our analysis shows that the WaveNet and LSTM model is able to better capture the overall trend and learn the inherent long and short term dependencies in time series data. Using this method we forecast 11 years of monthly averaged data for Solar Cycle 25. Our forecasts show that the upcoming Solar Cycle 25 will have a maximum sunspot number around 106 $\pm$ 19.75 and maximum total sunspot area around 1771 $\pm$ 381.17. This indicates that the cycle would be slightly weaker than Solar Cycle 24.
