# Large language models can be zero-shot anomaly detectors for time series?

arXiv: https://arxiv.org/abs/2405.14755 · HTML: https://mlsanity.com/paper/2405.14755/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 34 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [Generating Images with Multimodal Language Models](https://mlsanity.com/paper/2305.17216/) (2023)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://mlsanity.com/paper/2210.08964/) (2022)
- [Emergent and Predictable Memorization in Large Language Models](https://mlsanity.com/paper/2304.11158/) (2023)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://mlsanity.com/paper/2311.01933/) (2023)
- [Precision and Recall for Time Series](https://mlsanity.com/paper/1803.03639/) (2018)
- [TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks](https://mlsanity.com/paper/2009.07769/) (2020)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [AER: Auto-Encoder with Regression for Time Series Anomaly Detection](https://mlsanity.com/paper/2212.13558/) (2022)
- [OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User](https://mlsanity.com/paper/2310.17748/) (2023)

## Built on by (most cited first, 30 of 31)

- [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://mlsanity.com/paper/2412.03104/) (2024)
- [MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering](https://mlsanity.com/paper/2503.16858/) (2025)
- [Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2409.01980/) (2024)
- [A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models](https://mlsanity.com/paper/2509.11575/) (2025)
- [See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers](https://mlsanity.com/paper/2411.02465/) (2024)
- [Benchmarking Visual Language Models on Standardized Visualization Literacy Tests](https://mlsanity.com/paper/2503.16632/) (2025)
- [Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models](https://mlsanity.com/paper/2501.14170/) (2025)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Can Multimodal LLMs Perform Time Series Anomaly Detection?](https://mlsanity.com/paper/2502.17812/) (2025)
- [TSAQA: Time Series Analysis Question And Answering Benchmark](https://mlsanity.com/paper/2601.23204/) (2026)
- [AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning](https://mlsanity.com/paper/2602.13807/) (2026)
- [Adaptive Time Series Reasoning via Segment Selection](https://mlsanity.com/paper/2602.18645/) (2026)
- [Foundation Models for Anomaly Detection: Vision and Challenges](https://mlsanity.com/paper/2502.06911/) (2025)
- [Large Language models for Time Series Analysis: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2506.11040/) (2025)
- [Refining Time Series Anomaly Detectors using Large Language Models](https://mlsanity.com/paper/2503.21833/) (2025)
- [Robustness is Important: Limitations of LLMs for Data Fitting](https://mlsanity.com/paper/2508.19563/) (2025)
- [Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection](https://mlsanity.com/paper/2501.05675/) (2025)
- [TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection](https://mlsanity.com/paper/2508.00047/) (2025)
- [SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Data](https://mlsanity.com/paper/2510.03962/) (2025)
- [Chain-of-thought Reviewing and Correction for Time Series Question Answering](https://mlsanity.com/paper/2512.22627/) (2025)
- [Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers](https://mlsanity.com/paper/2605.05725/) (2026)
- [Transforming Decoder-Only Transformers for Accurate WiFi-Telemetry Based Indoor Localization](https://mlsanity.com/paper/2505.15835/) (2025)
- [SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture](https://mlsanity.com/paper/2509.18123/) (2025)
- [ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts](https://mlsanity.com/paper/2510.04710/) (2025)
- [Physics-Informed Large Language Models for HVAC Anomaly Detection with Autonomous Rule Generation](https://mlsanity.com/paper/2510.17146/) (2025)
- [Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Models](https://mlsanity.com/paper/2510.22868/) (2025)
- [AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection](https://mlsanity.com/paper/2602.08868/) (2026)
- [IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems](https://mlsanity.com/paper/2606.01691/) (2026)
- [Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Management](https://mlsanity.com/paper/2607.25340/) (2026)
- [Structured Frequency-Domain Evidence for LLM-Based Time-Series Anomaly Detection](https://mlsanity.com/paper/2608.24113/) (2026)

## Abstract (arXiv)

Recent studies have shown the ability of large language models to perform a variety of tasks, including time series forecasting. The flexible nature of these models allows them to be used for many applications. In this paper, we present a novel study of large language models used for the challenging task of time series anomaly detection. This problem entails two aspects novel for LLMs: the need for the model to identify part of the input sequence (or multiple parts) as anomalous; and the need for it to work with time series data rather than the traditional text input. We introduce sigllm, a framework for time series anomaly detection using large language models. Our framework includes a time-series-to-text conversion module, as well as end-to-end pipelines that prompt language models to perform time series anomaly detection. We investigate two paradigms for testing the abilities of large language models to perform the detection task. First, we present a prompt-based detection method that directly asks a language model to indicate which elements of the input are anomalies. Second, we leverage the forecasting capability of a large language model to guide the anomaly detection process. We evaluated our framework on 11 datasets spanning various sources and 10 pipelines. We show that the forecasting method significantly outperformed the prompting method in all 11 datasets with respect to the F1 score. Moreover, while large language models are capable of finding anomalies, state-of-the-art deep learning models are still superior in performance, achieving results 30% better than large language models.