mlsanityData to 7 Oct 2026

TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

arXiv 2210.09693 ↗First posted 18 Oct 2022cs.LG cs.AIcited by 48

Not on the map. Why?

Abstract (arXiv)

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). Builds on 31 of its 64 references that are in the map. First posted on arXiv 18 Oct 2022.

Builds on

31 of 64 references.

References · 31

  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. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  5. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  6. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  9. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  10. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  11. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
  12. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
19 more
  1. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  2. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  3. 2020A review on outlier/anomaly detection in time series datacited by 187
  4. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  5. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
  6. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  7. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
  8. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  9. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  10. 2018RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Seriescited by 54
  11. 2020RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networkscited by 54
  12. 2018An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videoscited by 45
  13. 2020Rethinking Assumptions in Deep Anomaly Detectioncited by 45
  14. 2021Neural Contextual Anomaly Detection for Time Seriescited by 42
  15. 2017Automatic Anomaly Detection in the Cloud Via Statistical Learningcited by 25
  16. 2021CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platformscited by 24
  17. 2021AutoAI-TS: AutoAI for Time Series Forecastingcited by 15
  18. 2022Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketchingcited by 13
  19. 2022Online Time Series Anomaly Detection with State Space Gaussian Processescited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 17; 2024: 16; 2025: 9; 2026: 6.

48 papers.

Sort:

By area:

  1. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  2. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  3. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  4. 2024CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patchingcited by 91
  5. 2023RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Modelscited by 62
  6. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  7. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detectioncited by 26
  8. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 22
  9. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  10. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  11. 2023Explainable Time Series Anomaly Detection using Masked Latent Generative Modelingcited by 15
  12. 2023Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solutioncited by 12
36 more
  1. 2023ADGym: Design Choices for Deep Anomaly Detectioncited by 11
  2. 2023A Co-training Approach for Noisy Time Series Learningcited by 8
  3. 2025Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependenciescited by 8
  4. 2023GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecastingcited by 5
  5. 2025Decomposition-based multi-scale transformer framework for time series anomaly detectioncited by 5
  6. 2023Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter Viewcited by 4
  7. 2024Phase-driven Domain Generalizable Learning for Nonstationary Time Seriescited by 4
  8. 2023Robust Dominant Periodicity Detection for Time Series with Missing Datacited by 3
  9. 2023Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detectioncited by 3
  10. 2024Scalable Transformer for High Dimensional Multivariate Time Series Forecastingcited by 3
  11. 2025Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labelscited by 3
  12. 2023A Survey of Time Series Anomaly Detection Methods in the AIOps Domaincited by 2
  13. 2023Self-supervised Adaptive Pre-training of Multilingual Speech Models for Language and Dialect Identificationcited by 2
  14. 2024Cluster-Wide Task Slowdown Detection in Cloud Systemcited by 2
  15. 2024Towards Unbiased Evaluation of Time-series Anomaly Detectorcited by 2
  16. 2025RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Predictioncited by 1
  17. 2026Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzerscited by 1
  18. 2023Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adaptercited by 0
  19. 2023GMTR: Graph Matching Transformerscited by 0
  20. 2023DarkShot: Lighting Dark Images with Low-Compute and High-Qualitycited by 0
  21. 2024TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processingcited by 0
  22. 2024TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networkscited by 0
  23. 2024T-Pixel2Mesh: Combining Global and Local Transformer for 3D Mesh Generation from a Single Imagecited by 0
  24. 2024Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithmscited by 0
  25. 2024Towards General Deepfake Detection with Dynamic Curriculumcited by 0
  26. 2024Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphscited by 0
  27. 2025GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detectioncited by 0
  28. 2025Mask6D: Masked Pose Priors For 6D Object Pose Estimationcited by 0
  29. 2025CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detectioncited by 0
  30. 2025LPCVAE: A Conditional VAE with Long-Term Dependency and Probabilistic Time-Frequency Fusion for Time Series Anomaly Detectioncited by 0
  31. 2025Causal Convolutional Neural Networks as Finite Impulse Response Filterscited by 0
  32. 2026Strong Linear Baselines Strike Back: Closed-Form Linear Models as Gaussian Process Conditional Density Estimators for TSADcited by 0
  33. 2026Contextual and Seasonal LSTMs for Time Series Anomaly Detectioncited by 0
  34. 2026Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimationcited by 0
  35. 2026Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detectioncited by 0
  36. 2026HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputationcited by 0