DWNet: Dual‐Window Deep Neural Network for Time Series Prediction
Jin Fan, Yipan Huang, Ke Zhang, Sen Wang, Jinhua Chen, Baiping Chen · Complexity · 2021
Multivariate time series prediction is a very important task, which plays a huge role in climate, economy, and other fields. We usually use an Attention‐based Encoder‐Decoder network to deal with multivariate time series prediction because the attention mechanism makes it easier for the model to focus on the really important attributes. However, the Encoder‐Decoder network has the problem that the longer the length of the sequence is, the worse the prediction accuracy is, which means that the Encoder‐Decoder network cannot process long series and therefore cannot obtain detailed historical information. In this paper, we propose a dual‐window deep neural network (DWNet) to predict time series. The dual‐window mechanism allows the model to mine multigranularity dependencies of time series, such as local information obtained from a short sequence and global information obtained from a long sequence. Our model outperforms nine baseline methods in four different datasets.