Refining Time Series Anomaly Detectors using Large Language Models

• URL: http://arxiv.org/abs/2503.21833v1
• Date: Wed, 26 Mar 2025 23:41:49 GMT
• Title: Refining Time Series Anomaly Detectors using Large Language Models
• Abstract summary: Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing.<n>We study the use of multimodal large language models (LLMs) to partially automate this process.
• Score: 7.772452855185151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system

Related papers

• LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection [1.1852406625172216]
  Time series anomaly detection often suffers from sparse labels, complex temporal patterns, and costly expert annotation.<n>We propose a unified framework that integrates Large Language Model (LLM)-based potential functions for reward shaping with Reinforcement Learning (RL), Variational Autoencoder (VAE)-enhanced dynamic reward scaling, and active learning with label propagation.
  arXiv  Detail & Related papers  (2026-01-05T19:33:30Z)
• IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection [70.02774285130238]
  This paper explores the combination of rich text semantics with both image-level and pixel-level information from images.<n>We propose IAD-GPT, a novel paradigm based on MLLMs for Industrial Anomaly Detection.<n>Experiments on MVTec-AD and VisA datasets demonstrate our state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-10-16T02:48:05Z)
• SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Data [15.670885465827206]
  Time series anomaly detection plays a crucial role in a wide range of fields, such as healthcare and internet traffic monitoring.<n>The emergence of large language models (LLMs) offers new opportunities for detecting anomalies in the ubiquitous time series data.<n>We propose Soft Prompt Enhanced Anomaly Recognition (SPEAR), a novel approach to leverage LLMs for anomaly detection with soft prompts and quantization.
  arXiv  Detail & Related papers  (2025-10-04T22:20:48Z)
• DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models [60.713908578319256]
  We propose Direct Discrepancy Learning (DDL) to optimize the detector with task-oriented knowledge.<n>Built upon this, we introduce DetectAnyLLM, a unified detection framework that achieves state-of-the-art MGTD performance.<n>MIRAGE samples human-written texts from 10 corpora across 5 text-domains, which are then re-generated or revised using 17 cutting-edge LLMs.
  arXiv  Detail & Related papers  (2025-09-15T10:59:57Z)
• Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback [55.284574165467525]
  Time-series Reasoning for Anomaly (Time-RA) transforms classical time series anomaly detection into a generative, reasoning-intensive task.<n>Also, we introduce the first real-world multimodal benchmark dataset, RATs40K, explicitly annotated for anomaly reasoning.
  arXiv  Detail & Related papers  (2025-07-20T18:02:50Z)
• Can Multimodal LLMs Perform Time Series Anomaly Detection? [55.534264764673296]
  We propose VisualTimeAnomaly benchmark to evaluate MLLMs in time series anomaly detection (TSAD)<n>Our approach transforms time series numerical data into the image format and feed these images into various MLLMs.<n>In total, VisualTimeAnomaly contains 12.4k time series images spanning 3 scenarios and 3 anomaly granularities with 9 anomaly types across 8 MLLMs.
  arXiv  Detail & Related papers  (2025-02-25T03:37:43Z)
• See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers [23.701716999879636]
  Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data. We introduce a pioneering framework called the Time Series Anomaly Multimodal Analyzer (TAMA) to enhance both the detection and interpretation of anomalies.
  arXiv  Detail & Related papers  (2024-11-04T10:28:41Z)
• OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data [0.0]
  We propose OML-AD, a novel approach for anomaly detection based on online machine learning (OML) We show that OML-AD outperforms state-of-the-art baseline methods in terms of accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-09-15T14:19:19Z)
• Anomaly Detection of Tabular Data Using LLMs [54.470648484612866]
  We show that pre-trained large language models (LLMs) are zero-shot batch-level anomaly detectors. We propose an end-to-end fine-tuning strategy to bring out the potential of LLMs in detecting real anomalies.
  arXiv  Detail & Related papers  (2024-06-24T04:17:03Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection [34.40206965758026]
  Time series anomaly detection (TSAD) plays a crucial role in various industries by identifying atypical patterns that deviate from standard trends. Traditional TSAD models, which often rely on deep learning, require extensive training data and operate as black boxes. We propose LLMAD, a novel TSAD method that employs Large Language Models (LLMs) to deliver accurate and interpretable TSAD results.
  arXiv  Detail & Related papers  (2024-05-24T09:07:02Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving [80.6725125503521]
  Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest. We present a use case of using ML models for detecting faults during automated screwdriving operations.
  arXiv  Detail & Related papers  (2021-07-05T11:46:00Z)
• Building an Automated and Self-Aware Anomaly Detection System [0.0]
  It can be challenging to proactively monitor a large number of diverse and constantly changing time series for anomalies. Traditionally, variations in the data generation processes and patterns have required strong modeling expertise to create models that accurately flag anomalies. In this paper, we describe an anomaly detection system that overcomes this common challenge by keeping track of its own performance and making changes as necessary to each model without requiring manual intervention.
  arXiv  Detail & Related papers  (2020-11-10T11:19:07Z)
• ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series [0.27528170226206433]
  This paper introduces ReRe, a Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to predict and jointly determine whether or not an upcoming data point is anomalous. Experiments based on real-world time-series datasets demonstrate the good performance of ReRe in real-time anomaly detection.
  arXiv  Detail & Related papers  (2020-04-05T21:26:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.