Summary of Contrast Similarity-aware Dual-pathway Mamba For Multivariate Time Series Node Classification, by Mingsen Du et al.
Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification
by Mingsen Du, Meng Chen, Yongjian Li, Xiuxin Zhang, Jiahui Gao, Cun Ji, Shoushui Wei
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 CS-DPMamba model addresses the challenges of modeling long-range dependencies and obtaining similarities effectively and efficiently in multivariate time series (MTS) data. The model consists of two pathways: a temporal contrast learning module that captures dynamic similarity, and a bidirectional Mamba pathway that considers both short- and long-range dependencies. A Fast Dynamic Time Warping matrix is constructed to represent the relationships between MTS samples, which are then fed into a Kolmogorov-Arnold Network enhanced Graph Isomorphism Network for node classification. Experimental results on UEA MTS datasets demonstrate the superiority of CS-DPMamba in both supervised and semi-supervised settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our paper is about a new way to analyze special kinds of data called multivariate time series. This type of data comes from lots of sensors that collect information over time, like monitoring people’s health or tracking the internet of things. The challenge is that this data changes over time and has many dimensions, making it hard to understand patterns and relationships. We created a new model called CS-DPMamba that can capture these patterns and relationships more effectively. Our model uses two parts: one that learns about how similar each piece of data is, and another that looks at both short-term and long-term connections between the data. We tested our model on several datasets and showed that it’s better than other methods for classifying this type of data. |
Keywords
» Artificial intelligence » Classification » Semi supervised » Supervised » Time series » Tracking
