DWNet: Dual-Window Deep Neural Network for Time Series Prediction
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 foc...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/1125630 |
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| Summary: | 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. |
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| ISSN: | 1076-2787 1099-0526 |