Summary of Mgcp: a Multi-grained Correlation Based Prediction Network For Multivariate Time Series, by Zhicheng Chen et al.
MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series
by Zhicheng Chen, Xi Xiao, Ke Xu, Zhong Zhang, Yu Rong, Qing Li, Guojun Gan, Zhiqiang Xu, Peilin Zhao
First submitted to arxiv on: 30 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 A novel approach to multivariate time series prediction is introduced in this paper, addressing the limitations of current models by simultaneously learning correlations at multiple granularities. The Multi-Grained Correlations-based Prediction (MGCP) Network combines Adaptive Fourier Neural Operators and Graph Convolutional Networks to extract features from fine-grained, medium-grained, and coarse-grained levels. Adversarial training with an attention mechanism-based predictor and conditional discriminator is used to optimize prediction results at the coarse-grained level. Experimental results on real-world benchmark datasets demonstrate the effectiveness of MGCP in outperforming state-of-the-art time series prediction algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict multiple things happening over time, which is important for many everyday applications. The problem with current approaches is that they don’t look at all the different connections and patterns in the data. To fix this, researchers came up with an idea called MGCP (Multi-Grained Correlations-based Prediction). This model uses special tools to look at the data from different angles, like a fine-grained level or a coarse-grained level. It’s like taking a step back to see the big picture and then zooming in on the details. The results show that this new approach works really well and can even beat other methods that are currently being used. |
Keywords
* Artificial intelligence * Attention * Time series
