This research examines the use of Large Language Models (LLMs) in predicting time series, with a specific focus on the LLMTIME model. Despite the established effectiveness of LLMs in tasks such as text generation, language translation, and sentiment analysis, this study highlights the key challenges that large language models encounter in the context of time series prediction. We assess the performance of LLMTIME across multiple datasets and introduce classical almost periodic functions as time series to gauge its effectiveness. The empirical results indicate that while large language models can perform well in zero-shot forecasting for certain datasets, their predictive accuracy diminishes notably when confronted with diverse time series data and traditional signals. The primary finding of this study is that the predictive capacity of LLMTIME, similar to other LLMs, significantly deteriorates when dealing with time series data that contain both periodic and trend components, as well as when the signal comprises complex frequency components.

本研究探讨了大型语言模型（LLMs）在时间序列预测中的应用，重点分析了LLMTIME模型。研究显示，尽管LLMs在生成文本、翻译和情感分析等任务中表现良好，但在处理复杂时间序列数据时，其预测准确性显著下降，尤其是当数据包含周期性和趋势成分时。这一发现强调了针对时间序列的LLMs的局限性和面临的挑战。

对标准统计模型和大型语言模型在时间序列预测中的评估