It has been a hot and challenging topic to predict the chaotic time series in the medium-to-long term. We combine autoencoders and convolutional neural networks (AE-CNN) to capture the intrinsic certainty of chaotic time series. We utilize the transfer learning (TL) theory to improve the prediction performance in medium-to-long term. Thus, we develop a prediction scheme for chaotic time series-based AE-CNN and TL named AE-CNN-TL. Our experimental results show that the proposed AE-CNN-TL has much better prediction performance than any one of the following: AE-CNN, ARMA, and LSTM.

中文翻译：

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基于混沌时间序列的AE-CNN和转移学习的预测

预测中长期的混沌时间序列一直是一个热门且具有挑战性的话题。我们结合自动编码器和卷积神经网络（AE-CNN）来捕获混沌时间序列的内在确定性。我们利用迁移学习（TL）理论来提高中长期预测性能。因此，我们开发了一种基于混沌时间序列的AE-CNN和TL的预测方案，称为AE-CNN-TL。我们的实验结果表明，所提出的AE-CNN-TL的预测性能远优于以下任何一项：AE-CNN，ARMA和LSTM。