Summary of Parametric Augmentation For Time Series Contrastive Learning, by Xu Zheng et al.
Parametric Augmentation for Time Series Contrastive Learning
by Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma, Haifeng Chen, Mo Sha, Dongsheng Luo
First submitted to arxiv on: 16 Feb 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 paper proposes a novel approach to contrastive learning for time series data, called AutoTCL. The authors address the challenge of selecting meaningful augmentations for time series data, which is difficult due to its diverse and complex nature. To overcome this limitation, they develop an encoder-agnostic framework that utilizes parametric augmentation and information theory to summarize the most commonly used augmentations in a unified format. Experimental results on univariate forecasting tasks demonstrate competitive performance, with an average 6.5% reduction in mean squared error (MSE) and 4.7% in mean absolute error (MAE), compared to leading baselines. In classification tasks, AutoTCL achieves a 1.2% increase in average accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoTCL is a new way to learn from time series data. Right now, it’s hard to choose the right things to add or change in this kind of data because it can be very complex and varied. The researchers developed a framework that uses information theory and summaries to help with this problem. They tested their approach on predicting future values and classifying types of time series data. It performed well, beating other methods by a little bit. |
Keywords
* Artificial intelligence * Classification * Encoder * Mae * Mse * Time series
