Large language models (LLMs) have been applied in many fields with rapid development in recent years. As a classic machine learning task, time series forecasting has recently received a boost from LLMs. However, there is a research gap in the LLMs' preferences in this field. In this paper, by comparing LLMs with traditional models, many properties of LLMs in time series prediction are found. For example, our study shows that LLMs excel in predicting time series with clear patterns and trends but face challenges with datasets lacking periodicity. We explain our findings through designing prompts to require LLMs to tell the period of the datasets. In addition, the input strategy is investigated, and it is found that incorporating external knowledge and adopting natural language paraphrases positively affects the predictive performance of LLMs for time series. Overall, this study contributes to insight into the advantages and limitations of LLMs in time series forecasting under different conditions.

翻译：大语言模型近年来发展迅速，已被广泛应用于众多领域。作为经典的机器学习任务，时间序列预测近期从大语言模型中获得了性能提升。然而，关于大语言模型在该领域的偏好特性仍存在研究空白。本文通过对比大语言模型与传统模型，发现了大语言模型在时间序列预测中的诸多特性。例如，研究表明大语言模型擅长预测具有清晰模式和趋势特征的时间序列，但在处理缺乏周期性的数据集时面临挑战。我们通过设计提示词要求大语言模型识别数据集的周期来解释这些发现。此外，我们还研究了输入策略，发现融入外部知识和采用自然语言改写对提升大语言模型的时间序列预测性能具有积极影响。总体而言，本研究为深入理解大语言模型在不同条件下进行时间序列预测的优势与局限性提供了重要见解。