Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models

• URL: http://arxiv.org/abs/2506.00457v1
• Date: Sat, 31 May 2025 08:24:01 GMT
• Title: Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
• Authors: Junwoo Park, Hyuck Lee, Dohyun Lee, Daehoon Gwak, Jaegul Choo,
• Abstract summary: Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training.<n>Recent studies suggest that LLMs lack inherent effectiveness in forecasting.<n>Our experiments show that LLM-based zero-shot forecasters often struggle to achieve high accuracy due to their sensitivity to noise.
• Score: 32.30528039193554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecasting. While LLMs have shown potential in zero-shot forecasting through prompting alone, recent studies suggest that LLMs lack inherent effectiveness in forecasting. Given these conflicting findings, a rigorous validation is essential for drawing reliable conclusions. In this paper, we evaluate the effectiveness of LLMs as zero-shot forecasters compared to state-of-the-art domain-specific models. Our experiments show that LLM-based zero-shot forecasters often struggle to achieve high accuracy due to their sensitivity to noise, underperforming even simple domain-specific models. We have explored solutions to reduce LLMs' sensitivity to noise in the zero-shot setting, but improving their robustness remains a significant challenge. Our findings suggest that rather than emphasizing zero-shot forecasting, a more promising direction would be to focus on fine-tuning LLMs to better process numerical sequences. Our experimental code is available at https://github.com/junwoopark92/revisiting-LLMs-zeroshot-forecaster.

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