Summary of Generating Synthetic Time Series Data For Cyber-physical Systems, by Alexander Sommers and Somayeh Bakhtiari Ramezani and Logan Cummins and Sudip Mittal and Shahram Rahimi and Maria Seale and Joseph Jaboure
Generating Synthetic Time Series Data for Cyber-Physical Systems
by Alexander Sommers, Somayeh Bakhtiari Ramezani, Logan Cummins, Sudip Mittal, Shahram Rahimi, Maria Seale, Joseph Jaboure
First submitted to arxiv on: 12 Apr 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 research paper investigates the application of transformers, a dominant sequence model, for data augmentation in the time series domain. The authors identify a gap in current literature and propose an architecture that combines successful priors to tackle this challenge. They test their approach using a powerful metric assessing time-domain similarity. The results highlight the difficulties in this domain and suggest valuable directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores ways to improve deep learning models for time series data. Currently, there’s a lack of ideas on how to use transformers, which are really good at processing sequential data, for data augmentation in time series. To address this gap, the researchers combine different techniques and test their approach using a clever method that measures how similar two time series patterns are. The findings show that working with time series data is tricky and suggest some exciting areas for future research. |
Keywords
* Artificial intelligence * Data augmentation * Deep learning * Sequence model * Time series
