Summary of Dgcformer: Deep Graph Clustering Transformer For Multivariate Time Series Forecasting, by Qinshuo Liu et al.
DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
by Qinshuo Liu, Yanwen Fang, Pengtao Jiang, Guodong Li
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Deep Graph Clustering Transformer (DGCformer) is a novel approach to multivariate time series forecasting that combines the strengths of channel-dependent (CD) and channel-independent (CI) strategies. By grouping relevant variables using a graph convolutional network integrated with an autoencoder, DGCformer applies CD strategy to each group and CI strategy across groups. Experimental results on eight datasets demonstrate its superiority over state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The DGCformer is a new way to forecast multiple time series that takes advantage of the strengths of two different approaches. It first organizes similar variables together using a special kind of neural network, then uses one approach for each group and another approach across groups. This helps it make more accurate predictions than other methods. |
Keywords
» Artificial intelligence » Autoencoder » Clustering » Convolutional network » Neural network » Time series » Transformer
