DWNet: Dual-Window Deep Neural Network for Time Series Prediction

Jin Fan, Yipan Huang, Ke Zhang, Sen Wang, Jinhua Chen, Baiping Chen and Fei Xiong

Complexity, 2021, vol. 2021, 1-10

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:1125630

DOI: 10.1155/2021/1125630

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