测试科学与仪器2021，Vol.12Issue(3)：322-330,9.DOI:10.3969/j.issn.1674-8042.2021.03.010
基于AR_CLSTM的多元时间序列预测分析
Multivariate time series prediction based on AR_CLSTM
摘要
Abstract
Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.关键词
编解码/注意力机制/卷积/自回归模型/多元时间序列Key words
encoder decoder/attention mechanism/convolution/autoregression model/multivariate time series引用本文复制引用
乔钢柱,宿荣,张宏飞..基于AR_CLSTM的多元时间序列预测分析[J].测试科学与仪器,2021,12(3):322-330,9.基金项目
Shanxi Provincial Key Research and Development Program Project Fund(No.201703D111011) （No.201703D111011）
