Preeti
In the paper “Are Language Models Actually Useful for Time Series Forecasting?”, Mingtian Tan and colleagues challenge the effectiveness of large language models (LLMs) in time series forecasting. Through ablation studies, they found that removing the LLM component or replacing it with a basic attention layer did not degrade forecasting performance and, in some cases, even improved it. Despite the significant computational costs of LLMs, simpler models without the LLM component showed reduced training and inference times while maintaining or enhancing performance. LLMs were also found to be ineffective in capturing sequential dependencies in time series data and did not show benefits in few-shot settings. Alternative approaches using time series encoders with patching and attention structures performed as well as LLM-based forecasters, offering a more efficient and effective solution for time series forecasting.
The implications of the study suggest that LLMs are not suitable for time series forecasting, and researchers should focus on developing specialized models for this task. Avoiding LLMs can lead to cost savings in computational resources, making time series forecasting more accessible and sustainable. The findings of the study serve as a turning point in steering the research community away from LLMs towards models tailored to the unique characteristics of time series data. Overall, the paper provides a data-driven critique of LLMs in time series forecasting and emphasizes the need for specialized models in this domain.
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Source link: https://www.rebellionresearch.com/are-language-models-actually-useful-for-time-series-forecasting-a-review
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