Summary of Umambatsf: a U-shaped Multi-scale Long-term Time Series Forecasting Method Using Mamba, by Li Wu et al.
UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba
by Li Wu, Wenbin Pei, Jiulong Jiao, Qiang Zhang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 Multivariate Time series forecasting is a crucial task in various domains such as transportation, meteorology, and finance. Existing methods primarily rely on Transformer architectures with attention mechanisms to capture temporal dependencies. However, these models suffer from quadratic time complexity, limiting their scalability for long input sequences. To address this issue, we propose UmambaTSF, a novel framework that integrates multi-scale feature extraction capabilities of U-shaped encoder-decoder multilayer perceptrons (MLP) with Mamba’s long sequence representation. The Mamba blocks in the framework adopt a refined residual structure and adaptable design to capture unique temporal signals and flexible channel processing. In experiments, UmambaTSF achieves state-of-the-art performance on widely used benchmark datasets while maintaining linear time complexity and low memory consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to predict what might happen next in a series of events or data points. This is called forecasting, and it’s important for things like predicting weather patterns, traffic flow, or stock prices. Right now, the best way to do this uses something called Transformers, but they have a big problem: they get very slow when dealing with long sequences of data. To fix this, we created a new method called UmambaTSF that combines two ideas: one from state-space models and another from multilayer perceptrons (MLPs). Our approach is fast, good at predicting, and uses less memory than other methods. |
Keywords
» Artificial intelligence » Attention » Encoder decoder » Feature extraction » Time series » Transformer
