Summary of A Ssm Is Polymerized From Multivariate Time Series, by Haixiang Wu
A SSM is Polymerized from Multivariate Time Series
by Haixiang Wu
First submitted to arxiv on: 30 Sep 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 This paper presents a novel method for multivariate time series (MTS) forecasting, called Poly-Mamba. The authors draw inspiration from state space models (SSMs), but depart from traditional Transformer-based approaches by explicitly modeling the complex dependencies in MTS using Channel Dependency variations with Time (CDT). The proposed method involves approximating continuously updated functions using orthogonal function basis and projecting onto a multivariate orthogonal function space to describe CDT patterns. Two key components are introduced: Multivariate Orthogonal Polynomial Approximation (MOPA) for simplified implementation, and Linear Channel Mixing (LCM) for generating adaptive CDT patterns. Experimental results on six real-world datasets demonstrate Poly-Mamba’s superiority over state-of-the-art methods, particularly when dealing with large numbers of channels and complex correlations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict things that change over time, like weather or stock prices. Right now, most methods for doing this are based on something called Transformers, but they don’t fully take into account the different relationships between these changing things. The authors want to fix this by creating a new model that can handle all these relationships. They do this by using special functions and projecting them onto a special space. Two key parts of their method are simplifying this process with something called MOPA, and generating patterns for each changing thing separately. By doing this, they show that their method is better than others at predicting things in real-world situations. |
Keywords
» Artificial intelligence » Time series » Transformer
