Summary of Timedart: a Diffusion Autoregressive Transformer For Self-supervised Time Series Representation, by Daoyu Wang et al.
TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation
by Daoyu Wang, Mingyue Cheng, Zhiding Liu, Qi Liu, Enhong Chen
First submitted to arxiv on: 8 Oct 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 TimeDART, a self-supervised time series pre-training framework that unifies two generative paradigms to learn transferable representations. The method combines a causal Transformer encoder with patch-based embeddings to model long-term dynamic evolution and a denoising diffusion process to capture local patterns. TimeDART is optimized in an autoregressive manner, allowing it to account for both global and local sequence features. Experimental results on public datasets show that TimeDART outperforms previous methods for time series forecasting and classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TimeDART is a new way to learn from time series data without needing labeled information. It uses two different approaches to capture patterns in the data: one that looks at big trends and another that focuses on small details. By combining these approaches, TimeDART can learn more about the data than any single approach alone. The results show that this method is better than others for tasks like forecasting and classification. |
Keywords
» Artificial intelligence » Autoregressive » Classification » Diffusion » Encoder » Self supervised » Time series » Transformer
