Summary of Segment, Shuffle, and Stitch: a Simple Layer For Improving Time-series Representations, by Shivam Grover et al.
Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations
by Shivam Grover, Amin Jalali, Ali Etemad
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 neural network layer called Segment, Shuffle, and Stitch (S3) to improve representation learning in time-series models. Unlike existing approaches that keep the original temporal arrangement of time-steps, S3 creates non-overlapping segments, shuffles them optimally for the task, and re-attaches them to capture both the shuffled sequence and the original input. This modular layer can be stacked to achieve different levels of granularity and added to various neural architectures like CNNs or Transformers with minimal computation overhead. The authors demonstrate significant improvements in time-series classification, forecasting, and anomaly detection tasks on multiple datasets, with performance enhancements up to 68% compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to learn representations of time-series data by rearranging the order of the sequence. This can be helpful for certain types of data where there are strong dependencies between non-adjacent parts. The researchers create a simple neural network layer that can do this and show it works well on several different tasks. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Neural network » Representation learning » Time series
