Welcome to PyOD 3 documentation!

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Note

New in V3. Any AI agent can now run a complete anomaly detection workflow on your data. Just ask.

PyOD 3 is the most comprehensive Python library for anomaly detection. Four pillars:

Pillar	What it means
Multi-Modal	60 detectors across tabular, time series, graph, text, and image data, one API
Full Lifecycle	From raw data to explained anomalies and next-step guidance in a single call
Agentic	Ask in plain English, and AI agents run the full detection workflow without OD expertise
Most Used	38+ million downloads; benchmark-backed routing (ADBench, TSB-AD, BOND, NLP-ADBench)

Install

Core library (required for every activation path):

pip install pyod

Then pick the activation path that matches your agent stack:

# 1. Claude Code / Claude Desktop / Codex — enables the od-expert skill
pyod install skill              # Claude Code / Desktop: user-global (~/.claude/skills/)
pyod install skill --project    # Codex: project-local (./skills/, Codex has no user-global dir)

# 2. Any MCP-compatible LLM — requires the optional mcp extra
pip install pyod[mcp]
pyod mcp serve                 # alias for `python -m pyod.mcp_server`

# 3. Pure Python — no extra step
#    from pyod.utils.ad_engine import ADEngine

Run pyod info at any time to see version, detector counts, and the install state of each activation path. pyod info also detects which agent stack you have installed (~/.claude/ for Claude Code, ~/.codex/ for Codex) and recommends the right install command.

For conda, source install, dependency details, and troubleshooting, see the full installation guide. The legacy pyod-install-skill command from v3.0.0 still works as an alias for pyod install skill.

Outlier Detection with 5 Lines of Code (pip install pyod):

from pyod.models.iforest import IForest
clf = IForest()
clf.fit(X_train)
y_train_scores = clf.decision_scores_          # training anomaly scores
y_test_scores = clf.decision_function(X_test)   # test anomaly scores

Three ways to use PyOD:

Layer	Name	When to use	Entry point
1	Classic API	You know which detector you want	Layer 1: Tabular Anomaly Detection
2	ADEngine	You want PyOD to choose, compare, and assess automatically	Layer 2: ADEngine Lifecycle Orchestration
3	Agentic Investigation	You want an AI agent to drive OD through natural conversation	Layer 3: Agentic Investigation

Layers 2 and 3 are powered by ADEngine, PyOD’s lifecycle orchestration core. Layer 3 adds the od-expert skill that auto-activates in Claude Code, Codex, and MCP-compatible agents.

A real 5-turn agentic conversation on the UCI Cardiotocography dataset (1,831 recordings, 21 clinical features).

See Layer 3: Agentic Investigation for the full walkthrough.

How PyOD 3 gets triggered:

See the Install block above for setup instructions for all three activation paths. The legacy pyod-install-skill command from v3.0.0 still works as an alias for pyod install skill.

PyOD Ecosystem & Resources: ADBench (tabular benchmark) [AHHH+22] | TSB-AD (time series) [ALP24] | BOND (graph) [ALDZ+22] | NLP-ADBench (NLP) [ALLX+24] | AD-LLM (LLM-based AD) [AYNL+24] | Resources

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About PyOD

PyOD, established in 2017, is the longest-running and most widely used Python library for anomaly detection. With 38+ million downloads, it serves both academic research and commercial products worldwide.

V3 extends the library with ADEngine (lifecycle orchestration) and the od-expert skill (agentic workflow), while keeping the classic fit/predict API fully backward-compatible. V3 is built on SUOD [AZHC+21] for fast parallel training and numba JIT for per-model speedups.

Citing PyOD:

If you use PyOD in a scientific publication, we would appreciate citations to the following paper(s):

PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection is available as a preprint. If you use PyOD in a scientific publication, we would appreciate citations to the following paper:

@inproceedings{chen2025pyod,
  title={Pyod 2: A python library for outlier detection with llm-powered model selection},
  author={Chen, Sihan and Qian, Zhuangzhuang and Siu, Wingchun and Hu, Xingcan and Li, Jiaqi and Li, Shawn and Qin, Yuehan and Yang, Tiankai and Xiao, Zhuo and Ye, Wanghao and others},
  booktitle={Companion Proceedings of the ACM on Web Conference 2025},
  pages={2807--2810},
  year={2025}
}

PyOD paper is published in Journal of Machine Learning Research (JMLR) (MLOSS track).:

@article{zhao2019pyod,
    author  = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
    title   = {PyOD: A Python Toolbox for Scalable Outlier Detection},
    journal = {Journal of Machine Learning Research},
    year    = {2019},
    volume  = {20},
    number  = {96},
    pages   = {1-7},
    url     = {http://jmlr.org/papers/v20/19-011.html}
}

or:

Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.

For a broader perspective on anomaly detection, see our NeurIPS papers on ADBench [AHHH+22] and ADGym.

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Benchmarks

• ADBench [AHHH+22]: 30 algorithms on 57 tabular datasets. See comparison.

• NLP-ADBench [ALLX+24]: 19 methods on 8 text datasets. Two-step (embedding + detector) beats end-to-end.

• TSB-AD [ALP24]: 40 algorithms on 1070 time series datasets (NeurIPS 2024).

• BOND [ALDZ+22]: 14 graph anomaly detection algorithms on 14 datasets (NeurIPS 2022).

Implemented Algorithms

PyOD is organized into two functional groups: (i) Detection Algorithms, with dedicated subsections for tabular, time series, and graph data (EmbeddingOD inside the tabular table adds multi-modal support for text and image via foundation model encoders); and (ii) Utility Functions for data generation, evaluation, and lifecycle orchestration.

(i-a) Tabular & Multi-Modal Detection Algorithms :

Type	Abbr	Algorithm	Year	Class	Ref
Probabilistic	ECOD	Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions	2022	pyod.models.ecod.ECOD	[ALZH+22]
Probabilistic	COPOD	COPOD: Copula-Based Outlier Detection	2020	pyod.models.copod.COPOD	[ALZB+20]
Probabilistic	ABOD	Angle-Based Outlier Detection	2008	pyod.models.abod.ABOD	[AKZ+08]
Probabilistic	FastABOD	Fast Angle-Based Outlier Detection using approximation	2008	pyod.models.abod.ABOD	[AKZ+08]
Probabilistic	MAD	Median Absolute Deviation (MAD)	1993	pyod.models.mad.MAD	[AIH93]
Probabilistic	SOS	Stochastic Outlier Selection	2012	pyod.models.sos.SOS	[AJHuszarPvdH12]
Probabilistic	QMCD	Quasi-Monte Carlo Discrepancy outlier detection	2001	pyod.models.qmcd.QMCD	[AFM01]
Probabilistic	KDE	Outlier Detection with Kernel Density Functions	2007	pyod.models.kde.KDE	[ALLP07]
Probabilistic	Sampling	Rapid distance-based outlier detection via sampling	2013	pyod.models.sampling.Sampling	[ASB13]
Probabilistic	GMM	Probabilistic Mixture Modeling for Outlier Analysis		pyod.models.gmm.GMM	[AAgg15] [Ch.2]
Linear Model	PCA	Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes)	2003	pyod.models.pca.PCA	[ASCSC03]
Linear Model	KPCA	Kernel Principal Component Analysis	2007	pyod.models.kpca.KPCA	[AHof07]
Linear Model	MCD	Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores)	1999	pyod.models.mcd.MCD	[AHR04, ARD99]
Linear Model	CD	Use Cook’s distance for outlier detection	1977	pyod.models.cd.CD	[ACoo77]
Linear Model	OCSVM	One-Class Support Vector Machines	2001	pyod.models.ocsvm.OCSVM	[AScholkopfPST+01]
Linear Model	LMDD	Deviation-based Outlier Detection (LMDD)	1996	pyod.models.lmdd.LMDD	[AAAR96]
Proximity-Based	LOF	Local Outlier Factor	2000	pyod.models.lof.LOF	[ABKNS00]
Proximity-Based	COF	Connectivity-Based Outlier Factor	2002	pyod.models.cof.COF	[ATCFC02]
Proximity-Based	Incr. COF	Memory Efficient Connectivity-Based Outlier Factor (slower but reduce storage complexity)	2002	pyod.models.cof.COF	[ATCFC02]
Proximity-Based	CBLOF	Clustering-Based Local Outlier Factor	2003	pyod.models.cblof.CBLOF	[AHXD03]
Proximity-Based	LOCI	LOCI: Fast outlier detection using the local correlation integral	2003	pyod.models.loci.LOCI	[APKGF03]
Proximity-Based	HBOS	Histogram-based Outlier Score	2012	pyod.models.hbos.HBOS	[AGD12]
Proximity-Based	HDBSCAN	Density-based clustering based on hierarchical density estimates	2013	pyod.models.hdbscan.HDBSCAN	[ACMS13]
Proximity-Based	kNN	k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score	2000	pyod.models.knn.KNN	[AAP02, ARRS00]
Proximity-Based	AvgKNN	Average kNN (use the average distance to k nearest neighbors as the outlier score)	2002	pyod.models.knn.KNN	[AAP02, ARRS00]
Proximity-Based	MedKNN	Median kNN (use the median distance to k nearest neighbors as the outlier score)	2002	pyod.models.knn.KNN	[AAP02, ARRS00]
Proximity-Based	SOD	Subspace Outlier Detection	2009	pyod.models.sod.SOD	[AKKrogerSZ09]
Proximity-Based	ROD	Rotation-based Outlier Detection	2020	pyod.models.rod.ROD	[AABC20]
Outlier Ensembles	IForest	Isolation Forest	2008	pyod.models.iforest.IForest	[ALTZ08, ALTZ12]
Outlier Ensembles	INNE	Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles	2018	pyod.models.inne.INNE	[ABTA+18]
Outlier Ensembles	DIF	Deep Isolation Forest for Anomaly Detection	2023	pyod.models.dif.DIF	[AXPWW23]
Outlier Ensembles	FB	Feature Bagging	2005	pyod.models.feature_bagging.FeatureBagging	[ALK05]
Outlier Ensembles	LSCP	LSCP: Locally Selective Combination of Parallel Outlier Ensembles	2019	pyod.models.lscp.LSCP	[AZNHL19]
Outlier Ensembles	XGBOD	Extreme Boosting Based Outlier Detection (Supervised)	2018	pyod.models.xgbod.XGBOD	[AZH18]
Outlier Ensembles	LODA	Lightweight On-line Detector of Anomalies	2016	pyod.models.loda.LODA	[APevny16]
Outlier Ensembles	SUOD	SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection (Acceleration)	2021	pyod.models.suod.SUOD	[AZHC+21]
Neural Networks	AutoEncoder	Fully connected AutoEncoder (use reconstruction error as the outlier score)	2015	pyod.models.auto_encoder.AutoEncoder	[AAgg15] [Ch.3]
Neural Networks	VAE	Variational AutoEncoder (use reconstruction error as the outlier score)	2013	pyod.models.vae.VAE	[AKW13]
Neural Networks	Beta-VAE	Variational AutoEncoder (all customized loss term by varying gamma and capacity)	2018	pyod.models.vae.VAE	[ABHP+18]
Neural Networks	SO_GAAL	Single-Objective Generative Adversarial Active Learning	2019	pyod.models.so_gaal.SO_GAAL	[ALLZ+19]
Neural Networks	MO_GAAL	Multiple-Objective Generative Adversarial Active Learning	2019	pyod.models.mo_gaal.MO_GAAL	[ALLZ+19]
Neural Networks	DeepSVDD	Deep One-Class Classification	2018	pyod.models.deep_svdd.DeepSVDD	[ARVG+18]
Neural Networks	AnoGAN	Anomaly Detection with Generative Adversarial Networks	2017	pyod.models.anogan.AnoGAN	[ASSeebockW+17]
Neural Networks	ALAD	Adversarially learned anomaly detection	2018	pyod.models.alad.ALAD	[AZRF+18]
Neural Networks	DevNet	Deep Anomaly Detection with Deviation Networks	2019	pyod.models.devnet.DevNet	[APSVDH19]
Neural Networks	AE1SVM	Autoencoder-based One-class Support Vector Machine	2019	pyod.models.ae1svm.AE1SVM	[ANV19]
Graph-based	R-Graph	Outlier detection by R-graph	2017	pyod.models.rgraph.RGraph	[AYRV17]
Graph-based	LUNAR	LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks	2022	pyod.models.lunar.LUNAR	[AGHNN22]
Embedding-based	EmbeddingOD	Multi-modal anomaly detection via foundation model embeddings (text, image)	2025	pyod.models.embedding.EmbeddingOD	[ALLX+24]

Ensemble methods (IForest, INNE, DIF, FB, LSCP, LODA, SUOD, XGBOD) are included in the table above. Score combination functions (average, maximization, AOM, MOA, median, majority vote) are in pyod.models.combination.

(i-b) Time Series Anomaly Detection :

All time series detectors use the same fit/predict/decision_function API as tabular detectors, with one exception: MatrixProfile is transductive (train-only; use decision_scores_ and labels_ after fit(), no out-of-sample predict).

Input format: numpy array of shape (n_timestamps,) for univariate or (n_timestamps, n_channels) for multivariate. Each row is one timestep; columns are channels/features. Pandas DataFrames and lists are auto-converted. Output: decision_scores_ of shape (n_timestamps,) with one anomaly score per timestep.

Time series detection in 3 lines:

from pyod.models.ts_kshape import KShape      # or any TS detector
clf = KShape(window_size=20)
clf.fit(X_train)                               # shape (n_timestamps,) or (n_timestamps, n_channels)
scores = clf.decision_scores_                  # per-timestamp anomaly scores

Algorithm rankings from TSB-AD benchmark [ALP24] (NeurIPS 2024, 1070 datasets):

Type	Abbr	Algorithm	Year	Class	Ref
Windowed Bridge	TimeSeriesOD	Any PyOD detector on sliding windows of time series	2026	pyod.models.ts_od.TimeSeriesOD
Subsequence	MatrixProfile	Matrix Profile (STOMP): nearest-neighbor distance, transductive (train-only)	2016	pyod.models.ts_matrix_profile.MatrixProfile	[AYZU+16]
Frequency	SpectralResidual	Spectral Residual: FFT-based saliency detection	2019	pyod.models.ts_spectral_residual.SpectralResidual	[ARXW+19]
Clustering	KShape	k-Shape clustering for subsequence anomaly detection (#2 in TSB-AD)	2015	pyod.models.ts_kshape.KShape	[APG15]
Streaming	SAND	Streaming anomaly detection with drift adaptation (experimental)	2021	pyod.models.ts_sand.SAND	[ABPPF21]
Deep Learning	LSTMAD	LSTM prediction error with Mahalanobis distance scoring	2015	pyod.models.ts_lstm.LSTMAD	[AMVSA15]
Deep Learning	AnomalyTransformer	Transformer with association discrepancy (experimental)	2022	pyod.models.ts_anomaly_transformer.AnomalyTransformer	[AXWWL22]

(i-c) Graph Anomaly Detection (pip install pyod[graph]):

All graph detectors are transductive in v1: use decision_scores_ and labels_ after fit(). No out-of-sample predict. Input: PyG Data object with x (node features) and edge_index (COO edges). SCAN works without features.

Graph detection in 3 lines (pip install pyod[graph]):

from pyod.models.pyg_dominant import DOMINANT
clf = DOMINANT(hidden_dim=64, epochs=100)
clf.fit(data)                                  # PyG Data object
scores = clf.decision_scores_                  # per-node anomaly scores

Algorithm rankings from BOND benchmark [ALDZ+22] (NeurIPS 2022, 14 datasets):

Type	Abbr	Algorithm	Year	Class	Ref
GCN Autoencoder	DOMINANT	GCN AE, structure + attribute reconstruction (#1 BOND deep)	2019	pyod.models.pyg_dominant.DOMINANT	[ADLBL19]
Contrastive	CoLA	Contrastive self-supervised, local neighbor context (#2 BOND deep)	2022	pyod.models.pyg_cola.CoLA	[ALLP+22]
Contrastive+AE	CONAD	Contrastive with anomalous-view injection + dual reconstruction	2022	pyod.models.pyg_conad.CONAD	[AXHZ+22]
Attention AE	AnomalyDAE	GAT structure encoder + MLP attribute encoder	2020	pyod.models.pyg_anomalydae.AnomalyDAE	[AFZL20]
Motif AE	GUIDE	Dual GCN AE on original + triangle-motif adjacency	2021	pyod.models.pyg_guide.GUIDE	[AYZY+21]
Matrix Factor.	Radar	Residual analysis via matrix factorization	2017	pyod.models.pyg_radar.Radar	[ALDHL17]
Matrix Factor.	ANOMALOUS	Joint MF with Laplacian regularization	2018	pyod.models.pyg_anomalous.ANOMALOUS	[APLL+18]
Structural	SCAN	Structural clustering, no features needed	2007	pyod.models.pyg_scan.SCAN	[AXYFS07]

(ii) Utility Functions:

Type	Name	Function
Data	generate_data()	Synthesized data generation; normal data from multivariate Gaussian, outliers from uniform distribution
Data	generate_data_clusters()	Synthesized data generation in clusters for more complex patterns
Data	generate_ts_data()	Synthesized time series data with point and subsequence anomalies
Evaluation	evaluate_print()	Print ROC-AUC and Precision @ Rank n for a detector
Evaluation	precision_n_scores()	Calculate Precision @ Rank n
Utility	get_label_n()	Turn raw outlier scores into binary labels by assigning 1 to the top n scores
Stat	wpearsonr()	Calculate the weighted Pearson correlation of two samples
Encoding	resolve_encoder()	Resolve an encoder from a string, BaseEncoder instance, or callable
Encoding	SentenceTransformerEncoder	Encode text via sentence-transformers models (see pyod.utils)
Encoding	OpenAIEncoder	Encode text via OpenAI Embeddings API (see pyod.utils)
Encoding	HuggingFaceEncoder	Encode text or images via HuggingFace transformers (see pyod.utils)
Orchestration	ADEngine	Anomaly detection lifecycle engine: profiling, planning, execution, analysis, and reporting

API Cheatsheet & Reference

The following APIs are applicable for all detector models for easy use.

• pyod.models.base.BaseDetector.fit(): Fit detector. y is ignored in unsupervised methods.

• pyod.models.base.BaseDetector.decision_function(): Predict raw anomaly score of X using the fitted detector.

• pyod.models.base.BaseDetector.predict(): Predict if a particular sample is an outlier or not using the fitted detector.

• pyod.models.base.BaseDetector.predict_proba(): Predict the probability of a sample being outlier using the fitted detector.

• pyod.models.base.BaseDetector.predict_confidence(): Predict the model’s sample-wise confidence (available in predict and predict_proba).

Key Attributes of a fitted model:

• pyod.models.base.BaseDetector.decision_scores_: The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher scores.

• pyod.models.base.BaseDetector.labels_: The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies.

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References

[AAgg15] (1,2)

Charu C Aggarwal. Outlier analysis. In Data mining, 75–79. Springer, 2015.

[AABC20]

Yahya Almardeny, Noureddine Boujnah, and Frances Cleary. A novel outlier detection method for multivariate data. IEEE Transactions on Knowledge and Data Engineering, 2020.

[AAP02] (1,2,3)

Fabrizio Angiulli and Clara Pizzuti. Fast outlier detection in high dimensional spaces. In European Conference on Principles of Data Mining and Knowledge Discovery, 15–27. Springer, 2002.

[AAAR96]

Andreas Arning, Rakesh Agrawal, and Prabhakar Raghavan. A linear method for deviation detection in large databases. In KDD, volume 1141, 972–981. 1996.

[ABTA+18]

Tharindu R Bandaragoda, Kai Ming Ting, David Albrecht, Fei Tony Liu, Ye Zhu, and Jonathan R Wells. Isolation-based anomaly detection using nearest-neighbor ensembles. Computational Intelligence, 34(4):968–998, 2018.

[ABPPF21]

Paul Boniol, John Paparrizos, Themis Palpanas, and Michael J. Franklin. Sand: streaming subsequence anomaly detection. Proceedings of the VLDB Endowment, 14(10):1717–1729, 2021.

[ABKNS00]

Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. Lof: identifying density-based local outliers. In ACM sigmod record, volume 29, 93–104. ACM, 2000.

[ABHP+18]

Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner. Understanding disentangling in betvae. arXiv preprint arXiv:1804.03599, 2018.

[ACMS13]

Ricardo JGB Campello, Davoud Moulavi, and Jörg Sander. Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining, 160–172. Springer, 2013.

[ACoo77]

R Dennis Cook. Detection of influential observation in linear regression. Technometrics, 19(1):15–18, 1977.

[ADLBL19]

Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. Deep anomaly detection on attributed networks. In Proceedings of the 2019 SIAM International Conference on Data Mining, 594–602. SIAM, 2019.

[AFZL20]

Haoyi Fan, Fengbin Zhang, and Zuoyong Li. Anomalydae: dual autoencoder for anomaly detection on attributed networks. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, 747–756. 2020.

[AFM01]

Kai-Tai Fang and Chang-Xing Ma. Wrap-around l2-discrepancy of random sampling, latin hypercube and uniform designs. Journal of complexity, 17(4):608–624, 2001.

[AGD12]

Markus Goldstein and Andreas Dengel. Histogram-based outlier score (hbos): a fast unsupervised anomaly detection algorithm. KI-2012: Poster and Demo Track, pages 59–63, 2012.

[AGHNN22]

Adam Goodge, Bryan Hooi, See-Kiong Ng, and Wee Siong Ng. Lunar: unifying local outlier detection methods via graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, 6737–6745. 2022.

[AHHH+22] (1,2,3)

Songqiao Han, Xiyang Hu, Hailiang Huang, Mingqi Jiang, and Yue Zhao. Adbench: anomaly detection benchmark. arXiv preprint arXiv:2206.09426, 2022.

[AHR04]

Johanna Hardin and David M Rocke. Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator. Computational Statistics & Data Analysis, 44(4):625–638, 2004.

[AHXD03]

Zengyou He, Xiaofei Xu, and Shengchun Deng. Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9-10):1641–1650, 2003.

[AHof07]

Heiko Hoffmann. Kernel pca for novelty detection. Pattern recognition, 40(3):863–874, 2007.

[AIH93]

Boris Iglewicz and David Caster Hoaglin. How to detect and handle outliers. Volume 16. Asq Press, 1993.

[AJHuszarPvdH12]

JHM Janssens, Ferenc Huszár, EO Postma, and HJ van den Herik. Stochastic outlier selection. Technical Report, Technical report TiCC TR 2012-001, Tilburg University, Tilburg Center for Cognition and Communication, Tilburg, The Netherlands, 2012.

[AKW13]

Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

[AKKrogerSZ09]

Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek. Outlier detection in axis-parallel subspaces of high dimensional data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 831–838. Springer, 2009.

[AKZ+08] (1,2)

Hans-Peter Kriegel, Arthur Zimek, and others. Angle-based outlier detection in high-dimensional data. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 444–452. ACM, 2008.

[ALLP07]

Longin Jan Latecki, Aleksandar Lazarevic, and Dragoljub Pokrajac. Outlier detection with kernel density functions. In International Workshop on Machine Learning and Data Mining in Pattern Recognition, 61–75. Springer, 2007.

[ALK05]

Aleksandar Lazarevic and Vipin Kumar. Feature bagging for outlier detection. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 157–166. ACM, 2005.

[ALDHL17]

Jundong Li, Harsh Dani, Xia Hu, and Huan Liu. Radar: residual analysis for anomaly detection in attributed networks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2152–2158. 2017.

[ALLX+24] (1,2,3)

Yuangang Li, Jiaqi Li, Zhuo Xiao, Tiankai Yang, Yi Nian, Xiyang Hu, and Yue Zhao. Nlp-adbench: nlp anomaly detection benchmark. arXiv preprint arXiv:2412.04784, 2024.

[ALZB+20]

Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, and Xiyang Hu. COPOD: copula-based outlier detection. In IEEE International Conference on Data Mining (ICDM). IEEE, 2020.

[ALZH+22]

Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, and H. George Chen. Ecod: unsupervised outlier detection using empirical cumulative distribution functions. IEEE Transactions on Knowledge and Data Engineering, 2022.

[ALTZ08]

Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. Isolation forest. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, 413–422. IEEE, 2008.

[ALTZ12]

Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(1):3, 2012.

[ALDZ+22] (1,2,3)

Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, and others. Bond: benchmarking unsupervised outlier node detection on static attributed graphs. In Advances in Neural Information Processing Systems, volume 35. 2022.

[ALP24] (1,2,3)

Qinghua Liu and John Paparrizos. Tsb-ad: the elephant in the room: towards a reliable time-series anomaly detection benchmark. In Advances in Neural Information Processing Systems (NeurIPS). 2024.

[ALLZ+19] (1,2)

Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, and Xiangnan He. Generative adversarial active learning for unsupervised outlier detection. IEEE Transactions on Knowledge and Data Engineering, 2019.

[ALLP+22]

Yixin Liu, Zhao Li, Shirui Pan, Tao Gool, Tao Xiang, and Boqing Gong. Anomaly detection on attributed networks via contrastive self-supervised learning. In Proceedings of the ACM Web Conference 2022, 2137–2147. 2022.

[AMVSA15]

Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. Long short term memory networks for anomaly detection in time series. In European Symposium on Artificial Neural Networks (ESANN). 2015.

[ANV19]

Minh-Nghia Nguyen and Ngo Anh Vien. Scalable and interpretable one-class svms with deep learning and random fourier features. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I 18, 157–172. Springer, 2019.

[APSVDH19]

Guansong Pang, Chunhua Shen, and Anton Van Den Hengel. Deep anomaly detection with deviation networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 353–362. 2019.

[APKGF03]

Spiros Papadimitriou, Hiroyuki Kitagawa, Phillip B Gibbons, and Christos Faloutsos. Loci: fast outlier detection using the local correlation integral. In Data Engineering, 2003. Proceedings. 19th International Conference on, 315–326. IEEE, 2003.

[APG15]

John Paparrizos and Luis Gravano. K-shape: efficient and accurate clustering of time series. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 1855–1870. 2015.

[APLL+18]

Zhen Peng, Minnan Luo, Jundong Li, Huan Liu, and Qinghua Zheng. Anomalous: a joint modeling approach for anomaly detection on attributed networks. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 3529–3535. 2018.

[APVD20]

Lorenzo Perini, Vincent Vercruyssen, and Jesse Davis. Quantifying the confidence of anomaly detectors in their example-wise predictions. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 227–243. Springer, 2020.

[APevny16]

Tomáš Pevn\`y. Loda: lightweight on-line detector of anomalies. Machine Learning, 102(2):275–304, 2016.

[ARRS00] (1,2,3)

Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim. Efficient algorithms for mining outliers from large data sets. In ACM Sigmod Record, volume 29, 427–438. ACM, 2000.

[ARXW+19]

Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang. Time-series anomaly detection service at microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3009–3017. 2019.

[ARD99]

Peter J Rousseeuw and Katrien Van Driessen. A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3):212–223, 1999.

[ARVG+18]

Lukas Ruff, Robert Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. Deep one-class classification. International conference on machine learning, 2018.

[ASSeebockW+17]

Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging, 146–157. Springer, 2017.

[AScholkopfPST+01]

Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. Estimating the support of a high-dimensional distribution. Neural computation, 13(7):1443–1471, 2001.

[ASCSC03]

Mei-Ling Shyu, Shu-Ching Chen, Kanoksri Sarinnapakorn, and LiWu Chang. A novel anomaly detection scheme based on principal component classifier. Technical Report, MIAMI UNIV CORAL GABLES FL DEPT OF ELECTRICAL AND COMPUTER ENGINEERING, 2003.

[ASB13]

Mahito Sugiyama and Karsten Borgwardt. Rapid distance-based outlier detection via sampling. Advances in neural information processing systems, 2013.

[ATCFC02] (1,2)

Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, and David W Cheung. Enhancing effectiveness of outlier detections for low density patterns. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 535–548. Springer, 2002.

[AXPWW23]

Hongzuo Xu, Guansong Pang, Yijie Wang, and Yongjun Wang. Deep isolation forest for anomaly detection. IEEE Transactions on Knowledge and Data Engineering, ():1–14, 2023. doi:10.1109/TKDE.2023.3270293.

[AXWWL22]

Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long. Anomaly transformer: time series anomaly detection with association discrepancy. In International Conference on Learning Representations (ICLR). 2022.

[AXYFS07]

Xiaowei Xu, Nurcan Yuruk, Zhidan Feng, and Thomas A.J. Schweiger. Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 824–833. 2007.

[AXHZ+22]

Zhiming Xu, Xiao Huang, Yue Zhao, Yushun Dong, and Jundong Li. Contrastive attributed network anomaly detection with data augmentation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 444–457. Springer, 2022.

[AYNL+24]

Tiankai Yang, Yi Nian, Shawn Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan Rossi, Kaize Ding, and others. Ad-llm: benchmarking large language models for anomaly detection. arXiv preprint arXiv:2412.11142, 2024.

[AYZU+16]

Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nusrat Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. Matrix profile i: all pairs similarity joins for time series subsequences. In 2016 IEEE 16th International Conference on Data Mining (ICDM), 1317–1322. IEEE, 2016.

[AYRV17]

Chong You, Daniel P Robinson, and René Vidal. Provable self-representation based outlier detection in a union of subspaces. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3395–3404. 2017.

[AYZY+21]

Xu Yuan, Na Zhou, Shuo Yu, Huafei Huang, Zhikui Chen, and Feng Xia. Higher-order structure based anomaly detection on attributed networks. In 2021 IEEE International Conference on Big Data, 2691–2700. IEEE, 2021.

[AZRF+18]

Houssam Zenati, Manon Romain, Chuan-Sheng Foo, Bruno Lecouat, and Vijay Chandrasekhar. Adversarially learned anomaly detection. In 2018 IEEE International conference on data mining (ICDM), 727–736. IEEE, 2018.

[AZH18]

Yue Zhao and Maciej K Hryniewicki. Xgbod: improving supervised outlier detection with unsupervised representation learning. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2018.

[AZHC+21] (1,2)

Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, and Leman Akoglu. Suod: accelerating large-scale unsupervised heterogeneous outlier detection. Proceedings of Machine Learning and Systems, 2021.

[AZNHL19]

Yue Zhao, Zain Nasrullah, Maciej K Hryniewicki, and Zheng Li. LSCP: locally selective combination in parallel outlier ensembles. In Proceedings of the 2019 SIAM International Conference on Data Mining, SDM 2019, 585–593. Calgary, Canada, May 2019. SIAM. URL: https://doi.org/10.1137/1.9781611975673.66, doi:10.1137/1.9781611975673.66.