Summary of Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting, by Jinliang Deng et al.
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
by Jinliang Deng, Feiyang Ye, Du Yin, Xuan Song, Ivor W. Tsang, Hui Xiong
First submitted to arxiv on: 22 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 model achieves superior and robust results across various datasets by containing excessive model inflation through decomposition. This method tailors decomposition to the intrinsic dynamics of time series data, outperforming existing benchmarks while using fewer parameters. The study highlights that in long-term time series forecasting (LTSF), bigger is not always better, and that a restricted set of parameters can be more effective when capitalizing on domain characteristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our study shows that you don’t need to make your model super complicated to get good results. We found that by breaking down the data into smaller pieces and understanding how it changes over time, we can make really accurate predictions using much fewer “building blocks” than usual. This is important because if models are too complex, they can be hard to understand and use. Our method works well across different types of data and performs better than other methods while using many fewer parameters. |
Keywords
* Artificial intelligence * Time series
