# Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

arXiv: https://arxiv.org/abs/2207.12201 · HTML: https://mlsanity.com/paper/2207.12201/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 65 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [Classification-Based Anomaly Detection for General Data](https://mlsanity.com/paper/2005.02359/) (2020)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [Deep Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/1911.08623/) (2019)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [A Survey of Label-noise Representation Learning: Past, Present and Future](https://mlsanity.com/paper/2011.04406/) (2020)
- [DROCC: Deep Robust One-Class Classification](https://mlsanity.com/paper/2002.12718/) (2020)
- [Neural Transformation Learning for Deep Anomaly Detection Beyond Images](https://mlsanity.com/paper/2103.16440/) (2021)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection](https://mlsanity.com/paper/2003.06780/) (2020)
- [Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection](https://mlsanity.com/paper/1806.04808/) (2018)
- [An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series](https://mlsanity.com/paper/2109.11428/) (2021)
- [Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding](https://mlsanity.com/paper/2011.14097/) (2020)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series](https://mlsanity.com/paper/1812.01767/) (2018)
- [Latent Outlier Exposure for Anomaly Detection with Contaminated Data](https://mlsanity.com/paper/2202.08088/) (2022)
- [Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version](https://mlsanity.com/paper/2111.11108/) (2021)
- [Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering](https://mlsanity.com/paper/2106.07992/) (2021)
- [Perturbation Learning Based Anomaly Detection](https://mlsanity.com/paper/2206.02704/) (2022)

## Built on by (most cited first, 30 of 40)

- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2308.09296/) (2023)
- [NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation](https://mlsanity.com/paper/2311.11961/) (2023)
- [Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution](https://mlsanity.com/paper/2311.11235/) (2023)
- [PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection](https://mlsanity.com/paper/2401.09793/) (2024)
- [DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2404.11269/) (2024)
- [SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection](https://mlsanity.com/paper/2405.11238/) (2024)
- [Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection](https://mlsanity.com/paper/2506.03964/) (2025)
- [GenIAS: Generator for Instantiating Anomalies in time Series](https://mlsanity.com/paper/2502.08262/) (2025)
- [RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data](https://mlsanity.com/paper/2503.18385/) (2025)
- [Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation](https://mlsanity.com/paper/2303.12973/) (2023)
- [Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities](https://mlsanity.com/paper/2308.14380/) (2023)
- [DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection](https://mlsanity.com/paper/2401.11271/) (2024)
- [Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2406.19770/) (2024)
- [Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2410.21322/) (2024)
- [Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning](https://mlsanity.com/paper/2502.03245/) (2025)
- [Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns](https://mlsanity.com/paper/2504.03750/) (2025)
- [TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection](https://mlsanity.com/paper/2508.00047/) (2025)
- [A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection](https://mlsanity.com/paper/2511.18739/) (2025)
- [Fortifying Time Series: DTW-Certified Robust Anomaly Detection](https://mlsanity.com/paper/2605.07690/) (2026)
- [Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs](https://mlsanity.com/paper/2606.18898/) (2026)
- [End-To-End Self-Tuning Self-Supervised Time Series Anomaly Detection](https://mlsanity.com/paper/2404.02865/) (2024)
- [Preference Isolation Forest for Structure-based Anomaly Detection](https://mlsanity.com/paper/2505.10876/) (2025)
- [A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks](https://mlsanity.com/paper/2506.00420/) (2025)
- [Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection](https://mlsanity.com/paper/2506.02757/) (2025)
- [CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection](https://mlsanity.com/paper/2509.01098/) (2025)
- [CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments](https://mlsanity.com/paper/2509.06419/) (2025)
- [A Graph Prompt Fine-Tuning Method for WSN Spatio-Temporal Correlation Anomaly Detection](https://mlsanity.com/paper/2601.12745/) (2026)
- [CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection](https://mlsanity.com/paper/2602.20468/) (2026)

## Abstract (arXiv)

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders.