基于AR＿CLSTM的多元时间序列预测分析

摘要：

时间序列是一种广泛应用于电量预测、汇率预测、太阳能发电量预测等各种领域的数据,预测其变化具有重要的意义。与LSTM相结合的编码器-解码器被广泛应用于多元时间序列预测。由于编码器只能将信息编码成固定长度的向量,因此模型的性能随着输入序列或输出序列长度的增加而迅速下降。为此,提出了基于编解码结构与线性回归的组合模型（ARCLSTM）,该模型使用基于时间步的注意力机制使解码器能够自适应选择过去的隐藏状态并提取有用的信息,并利用卷积的结构学习多元时间序列不同维度之间的内在联系,同时结合了传统的线性自回归方法来学习时间序列的线性关系,从而实现在编解码结构上进一步降低时间序列预测的误差,改善多元时间序列的预测效果。实验结果表明,ARCLSTM模型在不同的时间序列预测上表现良好,其均方根误差、均方误差、平均绝对误差均下降显著。

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.