mlsanityData to 14 Oct 2026

Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection

arXiv 2405.19823 ↗First posted 30 May 2024cs.LG cs.AIcited by 2

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Abstract (arXiv)

Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the ability of TSAD is limited by two key challenges: (i) the ability to model long-range dependency and (ii) the generalization issue in the presence of non-stationary data. To tackle these challenges, an anomaly detector that leverages the selective state space model known for its proficiency in capturing long-term dependencies across various domains is proposed. Additionally, a multi-stage detrending mechanism is introduced to mitigate the prominent trend component in non-stationary data to address the generalization issue. Extensive experiments conducted on realworld public datasets demonstrate that the proposed methods surpass all 12 compared baseline methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 30 May 2024.

Builds on

21 of 46 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  4. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  5. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  6. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  7. 2024VMamba: Visual State Space Modelcited by 969
  8. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  9. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  10. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  11. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 222
  12. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
9 more
  1. 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
  2. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
  3. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  4. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  5. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  6. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  7. 2024Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separationcited by 45
  8. 2020Master-Auxiliary: an efficient aggregation strategy for video anomaly detectioncited by 1
  9. 2023Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detectioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detectioncited by 0
  2. 2026Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series
  2. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
  3. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
  4. 2026GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection
  5. 2026CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection
  6. 2025Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning
  7. 2026LLM-Generated Feature Pools for Time Series Anomaly Detection
  8. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?