mlsanityData to 7 Oct 2026

TiSAT: Time Series Anomaly Transformer

arXiv 2203.05167 ↗First posted 10 Mar 2022cs.LG eess.SP stat.MLcited by 15

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Abstract (arXiv)

While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score. We show that a rudimentary Random Guess method can outperform state-of-the-art detectors in terms of this popular but faulty evaluation criterion. In this work, we propose a proper evaluation metric that measures the timeliness and precision of detecting sequential anomalies. Moreover, most existing approaches are unable to capture temporal features from long sequences. Self-attention based approaches, such as transformers, have been demonstrated to be particularly efficient in capturing long-range dependencies while being computationally efficient during training and inference. We also propose an efficient transformer approach for anomaly detection in time series and extensively evaluate our proposed approach on several popular benchmark datasets.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 23 of its 48 references that are in the map. First posted on arXiv 10 Mar 2022.

Builds on

23 of 48 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  5. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  6. 2020A Transformer-based Framework for Multivariate Time Series Representation Learningcited by 449
  7. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  8. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  9. 2017Dilated Residual Networkscited by 293
  10. 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
  11. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  12. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 213
11 more
  1. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  2. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  3. 2019An Attentive Survey of Attention Modelscited by 123
  4. 2017Attend and Diagnose: Clinical Time Series Analysis using Attention Modelscited by 105
  5. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  6. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 55
  7. 2017Deep and Confident Prediction for Time Series at Ubercited by 50
  8. 2019Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learningcited by 40
  9. 2017Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Networkcited by 28
  10. 2017Dilated Convolutions for Modeling Long-Distance Genomic Dependenciescited by 7
  11. 2020Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networkscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 1; 2026: 4.

15 papers.

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  1. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 38
  2. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  3. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  4. 2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstructioncited by 17
  5. 2024SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detectioncited by 6
  6. 2024Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directionscited by 6
  7. 2023Diagnostic Spatio-temporal Transformer with Faithful Encodingcited by 3
  8. 2024TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervisioncited by 1
  9. 2024Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyondcited by 1
  10. 2024PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Seriescited by 1
  11. 2025Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Seriescited by 1
  12. 2026Learning Unified Representations of Normalcy for Time Series Anomaly Detectioncited by 0
3 more
  1. 2026Weighted Score-Oriented Losses for Temporally Localized Event Predictioncited by 0
  2. 2026Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systemscited by 0
  3. 2026FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Predictioncited by 0