Summary of A Survey Of Deep Learning and Foundation Models For Time Series Forecasting, by John A. Miller et al.
A Survey of Deep Learning and Foundation Models for Time Series Forecasting
by John A. Miller, Mohammed Aldosari, Farah Saeed, Nasid Habib Barna, Subas Rana, I. Budak Arpinar, Ninghao Liu
First submitted to arxiv on: 25 Jan 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 The proposed paper surveys recent advances in deep learning for time series forecasting, highlighting the benefits of foundation models that can tap into existing knowledge graphs and large language models fine-tuned with scientific domain expertise. The authors discuss the limitations of current approaches, including the need for extensive training data and lack of interpretability, and propose incorporating external knowledge to improve model performance. The paper reviews state-of-the-art modeling techniques and suggests avenues for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how deep learning can be used for time series forecasting, especially in areas like pandemic prediction where traditional methods struggle. The authors show that foundation models, which are trained on a wide range of data before being fine-tuned for a specific task, can learn patterns and gain knowledge that helps them make predictions even when there’s not much training data available. They also discuss how other types of artificial intelligence models can be used to help deep learning models understand complex data better. |
Keywords
* Artificial intelligence * Deep learning * Time series
