Can LLMs Serve As Time Series Anomaly Detectors?

TL;DR

This paper explores the potential of large language models like GPT-4 and LLaMA3 to detect and explain anomalies in time series data, revealing that with proper prompting and fine-tuning, they can perform competitively with traditional methods.

Contribution

The study demonstrates how prompt strategies and instruction fine-tuning enable LLMs to effectively detect and explain time series anomalies, a novel application of LLMs beyond forecasting.

Findings

GPT-4 can detect anomalies with prompt strategies.

LLaMA3 improves detection after fine-tuning.

LLMs show promising potential as anomaly detectors.

Abstract

An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relevant but rarely explored and more challenging question is whether LLMs can detect and explain time series anomalies, a critical task across various real-world applications. In this paper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3, in detecting and explaining anomalies in time series. Our studies reveal that: 1) LLMs cannot be directly used for time series anomaly detection. 2) By designing prompt strategies such as in-context learning and chain-of-thought prompting, GPT-4 can detect time series anomalies with results competitive to baseline methods. 3) We propose a synthesized dataset to automatically generate time series anomalies with corresponding explanations. By applying instruction fine-tuning on this dataset, LLaMA3 demonstrates improved performance in time series anomaly detection tasks. In summary, our exploration shows the promising potential of LLMs as time series anomaly detectors.