大型语言模型对时间序列数据分析有用吗？
Are Large Language Models Useful for Time Series Data Analysis?
AI总结：
本研究通过对比LLM与非LLM方法在分类、异常检测和预测任务上的表现，发现LLM在异常检测中优势明显，但在预测中优势有限，为未来时间序列分析中LLM的应用与局限研究奠定基础。
摘要：
时间序列数据在医疗、能源和金融等多个领域发挥着关键作用，在这些领域中，分类、异常检测和预测等任务对于做出明智决策至关重要。近年来，大型语言模型（LLMs）因其处理复杂数据和提取有意义见解的能力而受到广泛关注。本研究通过将LLM方法与基于非LLM的方法在分类、异常检测和预测三个任务上的性能进行比较，探讨LLMs是否对时间序列数据分析有效。通过使用GPT4TS和自回归模型进行一系列实验，我们在基准数据集上评估了它们的性能，并评估了其准确性、精确度和泛化能力。我们的研究结果表明，虽然基于LLM的方法在异常检测等特定任务中表现出色，但在其他任务（如预测）中其优势不太明显，有时更简单的模型表现相当或更好。这项研究凸显了LLMs在时间序列分析中的作用，并为未来系统探索其在处理时间数据方面的应用和局限性奠定了基础。
英文摘要：
Time series data plays a critical role across diverse domains such as healthcare, energy, and finance, where tasks like classification, anomaly detection, and forecasting are essential for informed decision-making. Recently, large language models (LLMs) have gained prominence for their ability to handle complex data and extract meaningful insights. This study investigates whether LLMs are effective for time series data analysis by comparing their performance with non-LLM-based approaches across three tasks: classification, anomaly detection, and forecasting. Through a series of experiments using GPT4TS and autoregressive models, we evaluate their performance on benchmark datasets and assess their accuracy, precision, and ability to generalize. Our findings indicate that while LLM-based methods excel in specific tasks like anomaly detection, their benefits are less pronounced in others, such as forecasting, where simpler models sometimes perform comparably or better. This research highlights the role of LLMs in time series analysis and lays the groundwork for future studies to systematically explore their applications and limitations in handling temporal data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
