Summary of Mshyper: Multi-scale Hypergraph Transformer For Long-range Time Series Forecasting, by Zongjiang Shang et al.
MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting
by Zongjiang Shang, Ling Chen, Binqing Wu, Dongliang Cui
First submitted to arxiv on: 17 Jan 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 Multi-Scale Hypergraph Transformer (MSHyper) framework aims to improve long-range time series forecasting by modeling high-order interactions between temporal patterns of different scales. The approach introduces a multi-scale hypergraph and treats hyperedges as nodes, enabling the representation of complex pattern relationships. A tri-stage message passing mechanism is used to aggregate pattern information and learn interaction strengths. Experimental results on five real-world datasets demonstrate state-of-the-art performance across various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict future events based on past patterns is developed in this paper. The goal is to make more accurate predictions by understanding how different patterns interact with each other over time. To do this, a special type of graph called a hypergraph is used, which allows for the representation of complex relationships between patterns. This helps to capture high-order interactions that were previously not possible. Experimental results show that this new approach outperforms existing methods in predicting future events. |
Keywords
* Artificial intelligence * Time series * Transformer
