用预训练大模型检测多变量时间序列异常，解决模态不匹配问题。
MADLLM: Multivariate Anomaly Detection via Pre-trained LLMs
- 设计三重编码机制，将多变量时间序列映射到语言模型可理解的文本形式。
- 在多个公开数据集上优于现有方法，显著提升异常检测准确率。
- 适合需要高精度时序异常检测的研究与工业应用。
将预训练大语言模型（LLMs）应用于异常检测任务时，异常检测的多变量时间序列（MTS）模态与语言模型的文本模态不匹配。现有方法仅将MTS数据拆分为多个单变量序列，导致信息丢失。本文提出MADLLM，一种基于预训练LLMs的新型多变量异常检测方法。设计新的三重编码技术，将传统补丁嵌入与两种新嵌入方式结合：跳过嵌入（Skip Embedding），通过改变补丁处理顺序帮助模型保留历史特征知识；特征嵌入（Feature Embedding），利用对比学习增强模型对不同特征间相关性的理解。实验表明，该方法在多个公开异常检测数据集上均优于当前最先进方法。
原文摘要 · Abstract (English)
When applying pre-trained large language models (LLMs) to address anomaly detection tasks, the multivariate time series (MTS) modality of anomaly detection does not align with the text modality of LLMs. Existing methods simply transform the MTS data into multiple univariate time series sequences, which can cause many problems. This paper introduces MADLLM, a novel multivariate anomaly detection method via pre-trained LLMs. We design a new triple encoding technique to align the MTS modality with the text modality of LLMs. Specifically, this technique integrates the traditional patch embedding method with two novel embedding approaches: Skip Embedding, which alters the order of patch processing in traditional methods to help LLMs retain knowledge of previous features, and Feature Embedding, which leverages contrastive learning to allow the model to better understand the correlations between different features. Experimental results demonstrate that our method outperforms state-of-the-art methods in various public anomaly detection datasets.
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