Summary of Breaking the Context Bottleneck on Long Time Series Forecasting, by Chao Ma et al.
Breaking the Context Bottleneck on Long Time Series Forecasting
by Chao Ma, Yikai Hou, Xiang Li, Yinggang Sun, Haining Yu, Zhou Fang, Jiaxing Qu
First submitted to arxiv on: 21 Dec 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 proposed Logsparse Decomposable Multiscaling (LDM) framework is a multiscale modeling method designed to efficiently process long sequences for long-term time-series forecasting. By decoupling patterns at different scales in time series, LDM reduces non-stationarity, improves predictability, and simplifies architecture. Experimental results demonstrate that LDM outperforms baselines in long-term forecasting benchmarks while reducing training time and memory costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LDM is a new way to process long sequences for long-term time-series forecasting. It helps make predictions by looking at patterns at different scales. This makes the model more efficient, reduces non-stationarity, and simplifies its architecture. LDM is better than other methods in making long-term forecasts while using less training time and memory. |
Keywords
» Artificial intelligence » Time series
