Summary of Sst: Multi-scale Hybrid Mamba-transformer Experts For Long-short Range Time Series Forecasting, by Xiongxiao Xu et al.
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting
by Xiongxiao Xu, Canyu Chen, Yueqing Liang, Baixiang Huang, Guangji Bai, Liang Zhao, Kai Shu
First submitted to arxiv on: 23 Apr 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 paper addresses the issue of time series forecasting, highlighting the importance of tailoring objectives to different ranges (long-range and short-range) due to heterogeneity between them. The authors propose a multi-scale hybrid model called State Space Transformer (SST), which combines Mamba-Transformer experts for extracting global patterns in long-range time series and local variations in short-range time series. SST achieves superior performance, scaling linearly with time series length, while maintaining low memory footprint and computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making better predictions of what will happen in the future based on past data. The problem is that current methods don’t work well for very long or very short time periods. To solve this, the authors created a new model called State Space Transformer (SST) that can handle both long and short time periods. It uses two different parts to make predictions: one for global patterns in long-range data and another for local variations in short-range data. The results show that SST is better than other methods and takes less computer power. |
Keywords
» Artificial intelligence » Time series » Transformer
