mlsanityData to 7 Oct 2026

Contextual and Seasonal LSTMs for Time Series Anomaly Detection

arXiv 2602.09690 ↗First posted 10 Feb 2026cs.LGcited by 0

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Abstract (arXiv)

Univariate time series (UTS), where each timestamp records a single variable, serve as crucial indicators in web systems and cloud servers. Anomaly detection in UTS plays an essential role in both data mining and system reliability management. However, existing reconstruction-based and prediction-based methods struggle to capture certain subtle anomalies, particularly small point anomalies and slowly rising anomalies. To address these challenges, we propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs). CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns, thereby strengthening the detection of subtle anomalies. By integrating both time-domain and frequency-domain representations, CS-LSTMs achieve more accurate modeling of periodic trends and anomaly localization. Extensive evaluations on public benchmark datasets demonstrate that CS-LSTMs consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 65 references that are in the map. First posted on arXiv 10 Feb 2026.

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23 of 65 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  5. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  6. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
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  10. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  11. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  12. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
11 more
  1. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  2. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 87
  3. 2020Time Series Change Point Detection with Self-Supervised Contrastive Predictive Codingcited by 58
  4. 2018RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Seriescited by 54
  5. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 54
  6. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
  7. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 22
  8. 2024KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networkscited by 20
  9. 2022Constructing Large-Scale Real-World Benchmark Datasets for AIOpscited by 19
  10. 2021CAMul: Calibrated and Accurate Multi-view Time-Series Forecastingcited by 17
  11. 2023Robust Multimodal Failure Detection for Microservice Systemscited by 13

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