mlsanityData to 7 Oct 2026

Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback

arXiv 2507.15066 ↗First posted 20 Jul 2025cs.LG cs.AI cs.MMcited by 7

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Abstract (arXiv)

Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. To address these limitations, we propose Time-series Reasoning for Anomaly (Time-RA), a novel task that reformulates TSAD from a discriminative into a generative, reasoning-intensive paradigm. To facilitate this, we introduce RATs40K, the first real-world large-scale multimodal benchmark with ~40,000 samples across 10 domains, integrating raw time series, textual context, and visual plots with structured reasoning annotations. Extensive benchmarking shows that while supervised fine-tuning and visual representations boost diagnostic accuracy and reasoning consistency, performance varies across complex scenarios. Notably, fine-tuned models demonstrate strong "plug-and-play" transferability, outperforming traditional baselines on unseen real-world datasets. Our work establishes a foundation for interpretable, multimodal time series analysis. All code (https://github.com/yyysjz1997/Time-RA) and the RATs40K dataset (https://huggingface.co/datasets/Time-RA/RATs40K) are fully open-sourced to facilitate future research.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 28 of its 68 references that are in the map. First posted on arXiv 20 Jul 2025.

Builds on

28 of 68 references.

References · 28

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  4. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  5. 2018The UCR Time Series Archivecited by 421
  6. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  7. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 394
  8. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  9. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 234
  10. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  11. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  12. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
16 more
  1. 2020A review on outlier/anomaly detection in time series datacited by 187
  2. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  3. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
  4. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  5. 2024ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Datacited by 92
  6. 2025Time-MQA: Time Series Multi-Task Question Answering with Context Enhancementcited by 74
  7. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  8. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 54
  9. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  10. 2016Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Modelcited by 33
  11. 2024TimeX++: Learning Time-Series Explanations with Information Bottleneckcited by 32
  12. 2024Dive into Time-Series Anomaly Detection: A Decade Reviewcited by 23
  13. 2024Explaining Time Series via Contrastive and Locally Sparse Perturbationscited by 22
  14. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  15. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  16. 2021An Efficient One-Class SVM for Anomaly Detection in the Internet of Thingscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learningcited by 7
  2. 2026LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semanticscited by 3
  3. 2026ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolutioncited by 0
  4. 2026Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detectioncited by 0
  5. 2026Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructurecited by 0
  6. 2026EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detectioncited by 0
  7. 2026TraceBench: Controlled Evaluation of LLM Agents for Time-Series Root-Cause Attributioncited by 0