Summary of Unicl: a Universal Contrastive Learning Framework For Large Time Series Models, by Jiawei Li et al.
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models
by Jiawei Li, Jingshu Peng, Haoyang Li, Lei Chen
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents a new framework for pre-training time-series foundation models called UniCL (Universal and Scalable Contrastive Learning). The goal is to leverage unlabeled data to capture general patterns in time-series data, which can then be fine-tuned for specific tasks. Traditional supervised learning methods require extensive labeling of time-series data, making them impractical for real-world applications. Existing approaches to pre-training foundation models suffer from high bias and low generality due to the use of predefined augmentation operations and domain-specific data training. UniCL addresses these limitations by proposing a unified and trainable time-series augmentation operation that generates pattern-preserved, diverse, and low-bias data leveraging spectral information. The framework is designed for cross-domain pretraining, making it scalable for datasets with varying lengths. Experiments on two benchmark datasets across eleven domains validate the effectiveness of UniCL, demonstrating high generalization in time-series analysis across various fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to train machines to understand patterns in time-series data. Time-series data is used in many areas, like finance and healthcare, for tasks like forecasting and classification. Right now, we need to label lots of data for these tasks, which takes a lot of time and money. A better approach is to use pre-trained models that can learn from unlabeled data. This paper introduces UniCL, a new way to train these models using contrastive learning. It also proposes new methods to generate more diverse and low-bias data. The results show that UniCL works well across different domains and datasets. |
Keywords
» Artificial intelligence » Classification » Generalization » Pretraining » Supervised » Time series
