Are Language Models Actually Useful for Time Series Forecasting?
- URL: http://arxiv.org/abs/2406.16964v1
- Date: Sat, 22 Jun 2024 03:33:38 GMT
- Title: Are Language Models Actually Useful for Time Series Forecasting?
- Authors: Mingtian Tan, Mike A. Merrill, Vinayak Gupta, Tim Althoff, Thomas Hartvigsen,
- Abstract summary: Large language models (LLMs) are being applied to time series tasks, particularly time series forecasting.
We find that removing the LLM component or replacing it with a basic attention layer does not degrade the forecasting results.
We also find that pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings.
- Score: 21.378728572776897
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) are being applied to time series tasks, particularly time series forecasting. However, are language models actually useful for time series? After a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade the forecasting results -- in most cases the results even improved. We also find that despite their significant computational cost, pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings. Additionally, we explore time series encoders and reveal that patching and attention structures perform similarly to state-of-the-art LLM-based forecasters.
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