DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916245829320704 |
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| author | Liu, Qinshuo Fang, Yanwen Jiang, Pengtao Li, Guodong |
| author_facet | Liu, Qinshuo Fang, Yanwen Jiang, Pengtao Li, Guodong |
| contents | Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, and this even leads to worse performance than the channel-independent (CI) strategy. This paper combines the strengths of both strategies and proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. Specifically, it first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered with the CD strategy being applied to each group of variables while the CI one for different groups. Extensive experimental results on eight datasets demonstrate the superiority of our method against state-of-the-art models, and our code will be publicly available upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting Liu, Qinshuo Fang, Yanwen Jiang, Pengtao Li, Guodong Machine Learning Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, and this even leads to worse performance than the channel-independent (CI) strategy. This paper combines the strengths of both strategies and proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. Specifically, it first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered with the CD strategy being applied to each group of variables while the CI one for different groups. Extensive experimental results on eight datasets demonstrate the superiority of our method against state-of-the-art models, and our code will be publicly available upon acceptance. |
| title | DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.08440 |