Summary of Timemixer: Decomposable Multiscale Mixing For Time Series Forecasting, by Shiyu Wang et al.
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
by Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou
First submitted to arxiv on: 23 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 A novel approach to time series forecasting is proposed, which analyzes temporal variations by disentangling complex patterns at different scales. The methodology, called TimeMixer, uses a fully MLP-based architecture with Past-Decomposable-Mixing (PDM) and Future-Multipredictor-Mixing (FMM) blocks. PDM decomposes seasonal and trend components in fine-to-coarse and coarse-to-fine directions, aggregating microscopic seasonal and macroscopic trend information. FMM ensembles multiple predictors to utilize complementary forecasting capabilities. TimeMixer achieves state-of-the-art performances in both long-term and short-term forecasting tasks with favorable run-time efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is used in many applications like traffic planning and weather forecasting, but it’s challenging because real-world data has complex patterns. A new way to forecast time series is proposed that looks at patterns at different scales. This method uses a special kind of neural network called TimeMixer that breaks down the data into smaller parts and then mixes them together in a specific way. This helps to make predictions more accurate. The approach does well on both short-term and long-term forecasting tasks. |
Keywords
» Artificial intelligence » Neural network » Time series
