近日，一项由弗吉尼亚大学和华盛顿大学团队进行的研究对语言模型在时序预测任务中的表现提出了质疑。根据该研究，常用语言模型的方法在时序预测任务上的表现接近或劣于基本消融方法，而且所需计算量要多出几个数量级。
此前，不少研究者尝试将大型语言模型（LLM）应用于时间序列数据的分类、预测和异常检测。这些研究的假设是，语言模型能够泛化处理时间序列数据中顺序依赖关系的能力。然而，这项新研究的成果似乎打破了这一假设。
根据论文所述，LLM在处理时序数据时表现不佳的原因可能在于其无法有效地捕捉到时间序列数据的动态特性和时间序列数据独有的逻辑关联。这也使得一些人在看到这项研究后质疑语言模型是否真的适用于时序预测任务。尽管语言模型在文本处理领域取得了巨大的成功，但在时序预测领域的应用仍然面临挑战。这项研究为人们提供了一个重新审视语言模型在时序预测领域应用的角度，也为后续研究提供了新的思考方向。尽管语言模型在时序预测上的表现不尽如人意，但我们仍期待未来有新的突破和改进。毕竟，语言模型在其他领域的成功已经证明了其潜力巨大。对于未来的研究，我们期待看到更多关于如何将语言模型更好地应用于时序预测领域的探索和创新。
英语如下：
News Title: “Language Models Show Limited Performance in Time Series Prediction, Scholars Call for Re-examination of Application”
Keywords: LLM, Time Series Prediction, Poor Performance, High Computational Load
News Content:
Recently, a study conducted by teams from the University of Virginia and the University of Washington has raised doubts about the performance of language models in time series prediction tasks. According to the research, commonly used language modeling methods have demonstrated performance that is close to or inferior to basic ablation methods in time series prediction, and they require several orders of magnitude higher computational power.
Previously, many researchers had attempted to apply large language models (LLM) to the classification, prediction, and anomaly detection of time series data. The assumption behind these studies was that language models could generalize to handle the sequential dependencies in time series data. However, the findings of this new research seem to contradict this assumption.
According to the paper, the reasons why LLMs struggle when dealing with time series data might be due to their inability to effectively capture the dynamic characteristics and unique logical connections inherent in time series data. This has led some people to question whether language models are truly suitable for time series prediction tasks after seeing this research. Although language models have achieved tremendous success in the field of text processing, their application in time series prediction still faces challenges. This research provides a new perspective for re-examining the application of language models in time series prediction and a new direction for future research. Although language models have shown limited performance in time series prediction, we still expect future breakthroughs and improvements. After all, their success in other fields has already demonstrated their enormous potential. For future research, we look forward to seeing more exploration and innovation on how to better apply language models to time series prediction.
【来源】https://www.jiqizhixin.com/articles/2024-07-06-2
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