混乱的
计算机科学
均方误差
人工神经网络
卷积(计算机科学)
时间序列
算法
块(置换群论)
系列(地层学)
人工智能
卷积神经网络
相关系数
数学
机器学习
统计
古生物学
生物
几何学
作者
Wei Cheng,Yan Wang,Peng Zheng,Xiaodong Ren,Yubei Shuai,Shengyin Zang,Hao Liu,Hao Cheng,Jiagui Wu
标识
DOI:10.1016/j.chaos.2021.111304
摘要
The prediction of chaotic time series is important for both science and technology. In recent years, this type of prediction has improved significantly with the development of deep learning. Here, we propose a temporal convolutional network (TCN) model for the prediction of chaotic time series. Our TCN model offers highly stable training, high parallelism, and flexible perception field. Comparative experiments with the classic long short-term memory (LSTM) network and hybrid (CNN-LSTM) neural network show that the TCN model can reduce the training time by a factor of more than two. Furthermore, the network can focus on more important information because of the attention mechanism. By embedding the convolutional block attention module (CBAM), which combines the spatial and channel attention mechanisms, we obtain a new model, TCN-CBAM. This model is comprehensively better than the LSTM, CNN-LSTM, and TCN models in the prediction of classical systems (Chen system, Lorenz system, and sunspots). In terms of prediction accuracy, the TCN-CBAM model obtains better results for the four main evaluation indicators: root mean square error, mean absolute error, coefficient of determination, and Spearman's correlation coefficient, with a maximum increase of 41.4%. The TCN-CBAM has also the shortest training times among the previous classic four models.
科研通智能强力驱动
Strongly Powered by AbleSci AI