Summary of Rough Transformers: Lightweight and Continuous Time Series Modelling Through Signature Patching, by Fernando Moreno-pino et al.
Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching
by Fernando Moreno-Pino, Álvaro Arroyo, Harrison Waldon, Xiaowen Dong, Álvaro Cartea
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposes the Rough Transformer, a novel model architecture for processing time-series data with long-range dependencies and irregular sampling intervals. Traditional recurrent models struggle in these settings, while alternatives like Neural ODE-based models or Transformer-based architectures come at a high computational cost. The Rough Transformer operates on continuous-time representations of input sequences, reducing computation costs while capturing local and global dependencies through multi-view signature attention. This approach consistently outperforms vanilla attention counterparts on various time-series-related tasks, offering representational benefits similar to Neural ODE-based models but with significantly lower computational demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Rough Transformer is a new model that helps process time-series data better. Time-series data often has patterns and trends that last a long time, and it’s observed at different times. Current models have trouble with this type of data. The Rough Transformer uses special attention to look at both short-term and long-term patterns in the data, making it more efficient than other approaches. |
Keywords
» Artificial intelligence » Attention » Time series » Transformer
