Summary of Are Language Models Actually Useful For Time Series Forecasting?, by Mingtian Tan et al.
Are Language Models Actually Useful for Time Series Forecasting?
by Mingtian Tan, Mike A. Merrill, Vinayak Gupta, Tim Althoff, Thomas Hartvigsen
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A recent study investigates the effectiveness of large language models (LLMs) for time series forecasting. The researchers conducted ablation studies on three popular LLM-based methods and found that removing or replacing the LLM component did not degrade performance in most cases, with some even improving results. They also discovered that pretrained LLMs do not outperform models trained from scratch, nor do they capture sequential dependencies in time series or provide assistance in few-shot settings. Furthermore, the study compares time series encoders and finds that patching and attention structures perform similarly to LLM-based forecasters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper looks at whether language models are helpful for predicting future events based on past data. The answer is not a clear yes. Researchers tried removing or replacing parts of language model-based forecasting methods and found they still worked well. They also compared using pre-trained language models with ones trained from scratch and found no difference. Additionally, the study explores different ways to analyze time series data and finds that some approaches work just as well as others. |
Keywords
» Artificial intelligence » Attention » Few shot » Language model » Time series
