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 Long Short-Term Memory 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 ± 19.75 and maximum total sunspot area around 1771 ± 381.17. This indicates that the cycle would be slightly weaker than Solar Cycle 24.

中文翻译：

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使用深度神经网络预测太阳周期 25

随着机器学习领域的最新进展，使用深度神经网络进行时间序列预测变得更加普遍。太阳周期的准周期性特性使其成为应用时间序列预测方法的理想选择。我们采用 WaveNet 和长短期记忆神经网络的组合，使用 1749 年至 2019 年预测太阳黑子数量，使用 1874 年至 2019 年时间序列数据预测即将到来的太阳周期 25 的太阳黑子总面积。其他三个模型涉及LSTMs 和 1D ConvNets 的使用也与我们最好的模型进行了比较。我们的分析表明，WaveNet 和 LSTM 模型能够更好地捕捉整体趋势并学习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，我们预测了太阳周期 25 的 11 年月平均数据。我们的预测显示，即将到来的太阳活动周期 25 的最大太阳黑子数量约为 106 ± 19.75，最大太阳黑子总面积约为 1771 ± 381.17。这表明该周期将比太阳周期 24 稍弱。