Summary of Tada: Temporal Adversarial Data Augmentation For Time Series Data, by Byeong Tak Lee et al.
TADA: Temporal Adversarial Data Augmentation for Time Series Data
by Byeong Tak Lee, Joon-myoung Kwon, Yong-Yeon Jo
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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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 A novel approach to domain generalization in time series data is proposed, which enhances model robustness by incorporating temporal adversarial data augmentation (TADA) into the training process. Traditional adversarial data augmentation (ADA) techniques often fail to address distribution shifts related to temporal characteristics in time series data. To resolve this limitation, TADA incorporates time warping into ADA, leveraging duality between phase shifts in the frequency domain and time shifts in the time domain to make the process differentiable. Experimental results demonstrate that TADA outperforms existing methods for domain generalization across various time series datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help machines learn about things they haven’t seen before is being developed. This method, called Temporal Adversarial Data Augmentation (TADA), helps models deal with changes in patterns over time. Right now, most methods for this kind of learning don’t do a good job with time series data because they don’t account for how the data changes over time. TADA fixes this problem by adjusting the way it generates fake samples that mimic real-world situations. This makes the model better at dealing with unseen scenarios. |
Keywords
» Artificial intelligence » Data augmentation » Domain generalization » Time series
