DWNet: Dual-Window Deep Neural Network for Time Series Prediction
| Autor: | Yipan Huang, Ke Zhang, Sen Wang, Jinhua Chen, Jin Fan, Baiping Chen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Sequence
Multivariate statistics Multidisciplinary Article Subject General Computer Science Artificial neural network Series (mathematics) Computer science Process (computing) QA75.5-76.95 computer.software_genre Task (project management) Electronic computers. Computer science Data mining Time series Focus (optics) computer |
| Zdroj: | Complexity, Vol 2021 (2021) |
| ISSN: | 1099-0526 1076-2787 |
| DOI: | 10.1155/2021/1125630 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text