mlsanityData to 14 Oct 2026

LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

arXiv 2609.38789 ↗First posted 30 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

Reconstruction errors in multivariate time-series anomaly detection may not reliably distinguish abnormal behavior from benign deviations. Language-derived semantics offer complementary context, but existing multimodal approaches may rely on time-associated paired textual information that is difficult to obtain consistently and is not provided by standard multivariate time-series anomaly detection benchmarks. This setting poses two challenges: (1) conditioning masked reconstruction on window-specific semantics without exposing exact numerical targets or anomaly-specific cues, and (2) using a window-independent concept of normality as a complementary semantic reference rather than an independent anomaly detector. We propose LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Time Series (LEARN-TS), which uses a frozen language model to construct two role-separated semantic representations without requiring temporally paired external text. Window-specific observation semantics encode temporal and cross-variable context without exact numerical values to guide channel-shared patch-masked reconstruction. A fixed, dataset-agnostic normality prompt provides a window-independent semantic reference for aligning normal representations and estimating normality discrepancy. At inference, masking each temporal patch once yields timestamp-level reconstruction evidence, conditionally modulated by discrepancy from a separate unmasked view. Across four benchmarks, LEARN-TS achieves the highest mean performance in 13 of 16 dataset-metric comparisons. Controlled ablations examine observation conditioning, joint normality alignment and scoring, and reference content, showing dataset-dependent ranking benefits and modest average gains from semantic over random references.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 30 Sep 2026.

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17 of 29 references.

References · 17

  1. 2024Qwen2.5 Technical Reportcited by 4,942
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  4. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 700
  6. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  7. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  8. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  9. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 168
  10. 2024CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuningcited by 108
  11. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  12. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
5 more
  1. 2018Precision and Recall for Time Seriescited by 67
  2. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 38
  3. 2023MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detectioncited by 26
  4. 2024LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecastingcited by 8
  5. 2026Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interactioncited by 3

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  3. 2026Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series
  4. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
  5. 2025Refining Time Series Anomaly Detectors using Large Language Models
  6. 2024Can LLMs Understand Time Series Anomalies?
  7. 2024Can LLMs Serve As Time Series Anomaly Detectors?
  8. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection