Summary of Boosting Mlps with a Coarsening Strategy For Long-term Time Series Forecasting, by Nannan Bian and Minhong Zhu and Li Chen and Weiran Cai
Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
by Nannan Bian, Minhong Zhu, Li Chen, Weiran Cai
First submitted to arxiv on: 6 May 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 Coarsened Perceptron Network (CP-Net) aims to address the limitations of traditional multi-layer perceptrons (MLPs) in long-term time series forecasting, where they struggle to balance expressive power and computational efficiency. By introducing a coarsening strategy that forms information granules instead of relying on point-wise mapping, CP-Net extracts semantic and contextual patterns while preserving correlations over larger timespans and filtering out volatile noises. The model’s two-stage framework and multi-scale setting enable it to maintain a linear computational complexity and low runtime while achieving an improvement of 4.1% compared to the state-of-the-art (SOTA) method on seven forecasting benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new deep learning model called Coarsened Perceptron Network (CP-Net) that can do long-term time series forecasting better. The old way of doing this, using multi-layer perceptrons (MLPs), has problems because it doesn’t consider the bigger picture or filter out noisy information. CP-Net is different because it looks at patterns in a new way and uses a special technique to make predictions. This makes it faster and more accurate than other methods. |
Keywords
» Artificial intelligence » Deep learning » Time series
