Summary of Unlocking the Power Of Patch: Patch-based Mlp For Long-term Time Series Forecasting, by Peiwang Tang and Weitai Zhang
Unlocking the Power of Patch: Patch-Based MLP for Long-Term Time Series Forecasting
by Peiwang Tang, Weitai Zhang
First submitted to arxiv on: 22 May 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 This paper challenges the effectiveness of Transformer architectures in Long-Term Time Series Forecasting (LTSF) tasks, arguing that the Patch mechanism is overemphasized. Instead, simple linear layers with Patch enhancements may outperform complex Transformers. The study highlights the importance of cross-variable interactions and proposes a novel Patch-based MLP (PatchMLP) for LTSF tasks. By extracting smooth components and noise-containing residuals from time series data, engaging in semantic information interchange through channel mixing, and specializing in random noise, the PatchMLP model achieves state-of-the-art results on several real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper questions whether Transformers are the best solution for predicting future values in long-term time series. Researchers think that the Transformer’s ability to process sequential data is not fully utilized due to its self-attention mechanism. They suggest that simple models with a Patch mechanism might perform better than complex Transformers. The study also emphasizes the importance of considering relationships between different variables when making predictions. It proposes a new way to do this, called PatchMLP, which works well on real-world datasets. |
Keywords
» Artificial intelligence » Self attention » Time series » Transformer
