Summary of Fmamba: Mamba Based on Fast-attention For Multivariate Time-series Forecasting, by Shusen Ma et al.
FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting
by Shusen Ma, Yu Kang, Peng Bai, Yun-Bo Zhao
First submitted to arxiv on: 20 Jul 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 The paper proposes a novel framework for multivariate time-series forecasting (MTSF) called FMamba, which combines the strengths of two existing models: Mamba and Transformer-based predictive models. Mamba is a selective state space model that excels at extracting temporal features with linear computational complexity, while Transformer-based models excel at attending to relationships among variables but suffer from quadratic computational complexity. FMamba addresses this issue by integrating fast-attention with Mamba to enable the consideration of inter-variable dependencies. The framework consists of an embedding layer for feature extraction, a fast-attention module for computing dependencies, a multi-layer perceptron block (MLP-block) for extracting temporal features, and a projector layer for obtaining predictive results. Experimental results on eight public datasets demonstrate FMamba’s state-of-the-art performance while maintaining low computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FMamba is a new way to predict what will happen in the future based on patterns we see in past data. The problem is that current models are either very good at finding patterns but slow, or they’re fast but not as good. FMamba combines two different approaches to fix this. It takes in lots of information about things like temperature and weather, and then uses that to make predictions. This helps it do better than other models while still being efficient. |
Keywords
* Artificial intelligence * Attention * Embedding * Feature extraction * Temperature * Time series * Transformer
