用自然语言描述事件，让大模型分析不规则时间序列。
LAST SToP For Modeling Asynchronous Time Series
- 设计新提示词，利用事件描述的自然语言增强模型理解
- 在异常检测和数据补全任务上超越现有方法
- 适合需要跨领域推理的时间序列研究者
我们提出一种针对异步时间序列的新型提示设计方法。与固定间隔采样的常规时间序列不同，异步时间序列由不规则时间点上的时序事件组成，每个事件以自然语言描述。我们的方法充分利用事件描述中的丰富语义信息，使大语言模型能够借助其广泛的世界知识，在不同领域和任务间进行推理。这使得异步时间序列分析不再局限于预测，还可拓展至异常检测、数据补全等任务。我们进一步引入随机软提示（Stochastic Soft Prompting），一种新型提示调优机制，显著提升模型性能，优于现有微调方法如QLoRA。在多个真实世界数据集上的大量实验表明，该方法在不同任务和数据集上均达到领先水平。
原文摘要 · Abstract (English)
We present a novel prompt design for Large Language Models (LLMs) tailored to Asynchronous Time Series. Unlike regular time series, which assume values at evenly spaced time points, asynchronous time series consist of timestamped events occurring at irregular intervals, each described in natural language. Our approach effectively utilizes the rich natural language of event descriptions, allowing LLMs to benefit from their broad world knowledge for reasoning across different domains and tasks. This allows us to extend the scope of asynchronous time series analysis beyond forecasting to include tasks like anomaly detection and data imputation. We further introduce Stochastic Soft Prompting, a novel prompt-tuning mechanism that significantly improves model performance, outperforming existing fine-tuning methods such as QLoRA. Through extensive experiments on real world datasets, we demonstrate that our approach achieves state-of-the-art performance across different tasks and datasets.
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