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)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
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