School of Information and Control Engineering China University of Mining and Technology Xuzhou China

Department of Informatics University of Leicester Leicester UK

School of Computer Science and Technology University of Chinese Academic of Sciences Beijing China

State Key Laboratory of Digital Multimedia Chip Technology Beijing Vimicro Electronics Co. Ltd. Beijing China

Chongqing Vimicro AI Chip Technology Co. Ltd. Chongqing China

多元时间序列（MTS）预测旨在通过提取过去时间序列的多种依存关系来预测未来时间序列。传统的预测方法和基于深度学习的预测方法侧重于提取MTS某些方面的动态关系，尤其是时间特征，而常常忽略MTS的时空动态相关性。受卷积神经网络（CNN）和注意力机制的启发，本文提出了一种基于MTS预测并具有两阶段注意力的卷积LSTM网络模型。具体来说，我们首先提出一种新的MTS预处理方法，以更好地执行卷积运算。然后卷积层提取MTS的空间相关性，而LSTM模型提取时间相关性。值得一提的是，注意机制和LSTM的结合可以有效解决MTS预测中时间依赖性不足的问题。此外，双阶段注意机制可以有效地消除无关信息，选择相关的外源序列，赋予其更高的权重，并增加目标序列的过去值，从而进一步消除无关信息。最后，提取MTS时空相关性以提高预测准确性，并对该模型进行解释。实验结果表明该模型具有广阔的应用前景。根据财务，环境和能源的典型数据集进行的实验确定了预测的最佳窗口大小和隐藏大小，并证明与其他四个深度学习模型相比，该模型达到了最先进的效果。最重要的是，该模型不仅适用于MTS的单步预测，还适用于一定范围内时步的多步预测。 Multivariate time series (MTS) prediction aims at predicting future time series by extracting multiple forms of dependencies of past time series. Traditional prediction methods and deep learning‐based prediction methods focus on extracting the dynamic relationships of certain aspects of MTS, especially the temporal characteristics, often neglecting the spatial and temporal dynamic correlations of MTS. Inspired by convolution neural network (CNN) and attention mechanism, this paper proposes a convolution LSTM network model based on MTS prediction with two‐stage attention. Specifically, we first propose a new MTS preprocessing method to perform convolution operations better. Then convolution layer extracts spatial correlation of MTS and LSTM model extracts temporal correlation. It is worth mentioning that the combination of attention mechanism and LSTM can effectively solve the problem of insufficient time dependency in MTS prediction. In addition, dual‐stage attention mechanism can effectively eliminate irrelevant information, select the relevant exogenous sequence, give it higher weight, and increase the past value of the target sequence to further eliminate irrelevant information. Finally, the MTS spatio‐temporal correlation is extracted to improve the prediction accuracy, and the model is interpreted. Experimental results show that the model has broad application prospects. Experiments based on typical datasets of finance, environment, and energy determine the optimal window size and hidden size of the prediction, and demonstrate that the model achieves the state‐of‐the‐art effect compared to the other four deep learning models. On top of that, the model is not only suitable for single‐step prediction of MTS, but also suitable for multistep prediction of time step in a certain range.