利用LSTM人工神经网络预测太阳活动时间序列

B. Kozelov
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引用次数: 0

摘要

已经建立了一个预测太阳活动参数的数值模型——太阳黑子的数量R和未来27天10.7厘米F10.7波的辐射通量。数值模型采用具有LSTM(长短期记忆)层的人工神经网络(NN)。对于太阳黑子的数量和辐射通量,该模型预测了27天内数值变化的水平和极限。模型的平均绝对预测误差小于6%。实时模型在http://aurora.pgia.ru网站上实现,可以作为对其他INTERNET资源长期预测的补充。
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Prediction of solar activity time series using LSTM artificial neural network
A numerical model for predicting the parameters of solar activity — the number of sunspots R and the radioflux at a wave of 10.7 cm F10.7 ahead for 27 days — has been built. The numerical model uses an artificial neural network (NN) with LSTM (Long short-term memory) layers. For both the number of sunspots and the radioflux, the model predicts the levels and limits of variation of the values for 27 days. The average absolute prediction error of the model is less than 6 %. The real-time model is implemented on the site http://aurora.pgia.ru and can be an addition to long-term forecasts of other INTERNET resources.
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