Summary of Generative Pretrained Hierarchical Transformer For Time Series Forecasting, by Zhiding Liu et al.
Generative Pretrained Hierarchical Transformer for Time Series Forecasting
by Zhiding Liu, Jiqian Yang, Mingyue Cheng, Yucong Luo, Zhi Li
First submitted to arxiv on: 26 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 In this paper, researchers tackle two limitations in current time series forecasting methods. First, they show that most approaches rely on a single dataset for training, which restricts their ability to generalize to new data. Second, they argue that one-step generation schemas are inadequate because they don’t capture temporal dependencies and require customized forecasting heads. To address these issues, the authors propose novel architectures and self-supervised pretraining strategies that can leverage multiple datasets and handle varying time horizons. They demonstrate the effectiveness of their approach on several benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to improve time series forecasting by addressing two main problems. The first issue is that most methods only use one dataset for training, which makes them less useful in real-life situations. The second problem is that these models don’t take into account how the future values are connected to each other. To solve this, researchers are developing new architectures and ways of training models that can handle many datasets and different time horizons. |
Keywords
* Artificial intelligence * Pretraining * Self supervised * Time series
