Summary of Simplified Mamba with Disentangled Dependency Encoding For Long-term Time Series Forecasting, by Zixuan Weng et al.
Simplified Mamba with Disentangled Dependency Encoding for Long-Term Time Series Forecasting
by Zixuan Weng, Jindong Han, Wenzhao Jiang, Hao Liu
First submitted to arxiv on: 22 Aug 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 Medium Difficulty summary: Recent advances in deep learning have led to numerous models for Long-term Time Series Forecasting (LTSF), but most approaches struggle to capture reliable and informative dependencies in time series data. This paper identifies three critical dependencies – order, semantic, and cross-variate dependencies – that are rarely considered holistically in existing models. Improper handling of these dependencies can introduce noise, impairing forecasting performance. To address this, the authors explore Mamba for LTSF, highlighting its advantages in capturing each dependency. They propose SAMBA, a Simplified Mamba with disentangled dependency encoding, which eliminates nonlinearity and minimizes interference between time and variate dimensions. Extensive experiments on nine real-world datasets demonstrate SAMBA’s effectiveness over state-of-the-art forecasting models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about improving the accuracy of long-term predictions for things that happen in sequence, like stock prices or weather forecasts. The authors identify three important factors that make these predictions more accurate: how things are related to each other in time, what the data means, and how different pieces of information affect each other. They introduce a new model called SAMBA that can handle these factors better than other models. By testing their model on real-world data, they show that it is more effective at making accurate predictions. |
Keywords
» Artificial intelligence » Deep learning » Time series
