Summary of Higher-order Cross-structural Embedding Model For Time Series Analysis, by Guancen Lin et al.
Higher-order Cross-structural Embedding Model for Time Series Analysis
by Guancen Lin, Cong Shen, Aijing Lin
First submitted to arxiv on: 30 Oct 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 This paper proposes a novel framework, Higher-order Cross-structural Embedding Model for Time Series (High-TS), to analyze complex time series data. High-TS combines multiscale Transformer with Topological Deep Learning (TDL) to jointly model temporal and spatial dependencies, which is essential for capturing higher-order interactions within time series. The proposed method utilizes contrastive learning to integrate these two structures, generating robust and discriminative representations. Experimental results show that High-TS outperforms state-of-the-art methods in various time series tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to study time series data, which is important for many areas like healthcare, finance, and sensors. Time series are hard to understand because they change over time and have patterns that are hard to spot. Current methods try to find these patterns separately, but this limits how well they work. The new method, called High-TS, combines two ideas: one that looks at different scales of time and another that looks at the relationships between data points in space. This helps High-TS capture complex patterns within time series. Tests show that High-TS does better than other methods for certain tasks. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Time series » Transformer
