Summary of Softs: Efficient Multivariate Time Series Forecasting with Series-core Fusion, by Lu Han et al.
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
by Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan
First submitted to arxiv on: 22 Apr 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 This paper presents an efficient MLP-based model, SOFTS (Series-cOre Fused Time Series forecaster), for multivariate time series forecasting. The proposed model incorporates a novel STAR (STar Aggregate-Redistribute) module that aggregates all series to form a global core representation, which is then dispatched and fused with individual series representations to facilitate channel interactions. Unlike traditional approaches that manage channel interactions through distributed structures like attention, the STAR module employs a centralized strategy to improve efficiency and reduce reliance on the quality of each channel. The SOFTS model achieves superior performance over existing state-of-the-art methods with only linear complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops an innovative time series forecasting model called SOFTS that helps predict multiple related data streams, like stock prices or traffic flow. Existing models tried to capture relationships between these streams by spreading attention across them, but this made them complicated and vulnerable to changes in the data. The new SOFTS model takes a different approach, combining all the streams into one central representation, then blending it with individual stream representations. This makes it more efficient and better at handling changing conditions. The results show that SOFTS outperforms other top models while keeping calculations simple. |
Keywords
» Artificial intelligence » Attention » Time series
