Summary of Tscmamba: Mamba Meets Multi-view Learning For Time Series Classification, by Md Atik Ahamed et al.
TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification
by Md Atik Ahamed, Qiang Cheng
First submitted to arxiv on: 6 Jun 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 proposed multi-view approach for multivariate time series classification (TSC) integrates diverse features to capture patterns with properties like shift equivariance and inversion invariance. The method combines spectral, temporal, local, and global features using continuous wavelet transform and fusion with temporal convolutional or multilayer perceptron features. It also utilizes the Mamba state space model for efficient sequence modeling and introduces a new scanning scheme called tango scanning to leverage inversion invariance. Experimental results on benchmark datasets demonstrate average accuracy improvements of 4-7% compared to leading TSC models like TimesNet and TSLANet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to classify time series data is proposed, which helps with important tasks like tracking health trends or predicting stock prices. The approach looks at different types of patterns in the data, like what happens over short or long periods of time, and combines them to make a more accurate prediction. This method also helps the model stay robust even when new data comes in that’s similar but not identical to what it’s seen before. Tests on several datasets show that this approach works better than other popular methods. |
Keywords
» Artificial intelligence » Classification » Time series » Tracking
