Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series | Paper Citation Record

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This paper cites Aggarwal.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Aggarwal

Reference 1

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This paper cites Webb, Shirui Pan, Charu Aggarwal, and Mahsa Salehi.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Webb, Shirui Pan, Charu Aggarwal, and Mahsa Salehi

Reference 2

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This paper cites ADBench: Anomaly Detection Benchmark.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series ADBench: Anomaly Detection Benchmark

Reference 3

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This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 4

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This paper cites Aggarwal, and Jiawei Han.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Aggarwal, and Jiawei Han

Reference 5

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This paper cites Deep Learning for Anomaly Detection: A Survey.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Deep Learning for Anomaly Detection: A Survey

Reference 6

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 7

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This paper cites Anomaly detection in time series: a comprehensive evaluation.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Anomaly detection in time series: a comprehensive evaluation

Reference 8

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This paper cites Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art

Reference 9

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This paper cites Tsay, Themis Palpanas, and Michael J.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Tsay, Themis Palpanas, and Michael J

Reference 10

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This paper cites Isolation forest.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Isolation forest

Reference 11

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This paper cites Support vector method for novelty detection.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Support vector method for novelty detection

Reference 12

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This paper cites Cover and P.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Cover and P

Reference 13

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This paper cites Revisiting time series outlier detection: Definitions and benchmarks.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Revisiting time series outlier detection: Definitions and benchmarks

Reference 15

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This paper cites Anomaly detection: A survey.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Anomaly detection: A survey

Reference 16

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 17

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This paper cites Jennings.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Jennings

Reference 18

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This paper cites Graph neural network-based anomaly detection in multivariate time series.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Graph neural network-based anomaly detection in multivariate time series

Reference 19

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This paper cites Multivariate time-series anomaly detection via graph attention network.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Multivariate time-series anomaly detection via graph attention network

Reference 20

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This paper cites Generative Adversarial Networks.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Generative Adversarial Networks

Reference 21

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This paper cites Modeles connexionnistes de l’apprentissage.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Modeles connexionnistes de l’apprentissage

Reference 22

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This paper cites Bourlard and Y.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Bourlard and Y

Reference 23

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This paper cites Developing population codes by minimizing description length.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Developing population codes by minimizing description length

Reference 24

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This paper cites Auto-Encoding Variational Bayes.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Auto-Encoding Variational Bayes

Reference 25

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series The graph neural network model

Reference 26

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This paper cites McCulloch and Walter Pitts.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series McCulloch and Walter Pitts

Reference 27

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This paper cites Rumelhart, Geoffrey E.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Rumelhart, Geoffrey E

Reference 28

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This paper cites Long short-term memory.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Long short-term memory

Reference 29

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This paper cites Deep autoencoding gaussian mixture model for unsupervised anomaly detection.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Deep autoencoding gaussian mixture model for unsupervised anomaly detection

Reference 30

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This paper cites Robust anomaly detection for multivariate time series through stochastic recurrent neural network.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Robust anomaly detection for multivariate time series through stochastic recurrent neural network

Reference 31

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This paper cites Unsupervised online anomaly detection on multivariate sensing time series data for smart manufacturing.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unsupervised online anomaly detection on multivariate sensing time series data for smart manufacturing

Reference 32

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This paper cites Transformers in Time Series: A Survey.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Transformers in Time Series: A Survey

Reference 33

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This paper cites Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ravi P.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ravi P

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This paper cites Variational transformer- based anomaly detection approach for multivariate time series.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Variational transformer- based anomaly detection approach for multivariate time series

Reference 35

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This paper cites Unsupervised anomaly detection in multivariate time series through transformer-based variational autoencoder.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unsupervised anomaly detection in multivariate time series through transformer-based variational autoencoder

Reference 36

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This paper cites Learning graph structures with transformer for multivariate time series anomaly detection in IoT.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Learning graph structures with transformer for multivariate time series anomaly detection in IoT

Reference 37

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This paper cites Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Reference 38

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This paper cites Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism

Reference 39

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This paper cites Laptev, S.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Laptev, S

Reference 40

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This paper cites Unsupervised real-time anomaly detection for streaming data.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unsupervised real-time anomaly detection for streaming data

Reference 41

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This paper cites Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding

Reference 42

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This paper cites Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress

Reference 43

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This paper cites Mathur and Nils Ole Tippenhauer.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Mathur and Nils Ole Tippenhauer

Reference 44

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 45

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This paper cites Anomaly detection tutorial.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Anomaly detection tutorial

Reference 46

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This paper cites URL https://dl.acm.org/doi/10.1145/3055366.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series URL https://dl.acm.org/doi/10.1145/3055366

Reference 47

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This paper cites Internet of things: Online anomaly detection for drinking water quality.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Internet of things: Online anomaly detection for drinking water quality

Reference 48

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This paper cites Johnson, and Gianluca Bontempi.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Johnson, and Gianluca Bontempi

Reference 49

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series https://www.openml.org/search?type=data&sort=runs&id=1597&status=active

Reference 50

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This paper cites Deep Learning for Anomaly Detection: A Review.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Deep Learning for Anomaly Detection: A Review

Reference 51

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series IEEE-CIS fraud detection

Reference 52

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This paper cites A didactic approach to quantum machine learning with a single qubit.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series A didactic approach to quantum machine learning with a single qubit

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This paper cites Do We Really Need Deep Learning Models for Time Series Forecasting?.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Do We Really Need Deep Learning Models for Time Series Forecasting?

Reference 54

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This paper cites Towards a rigorous evaluation of time-series anomaly detection.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Towards a rigorous evaluation of time-series anomaly detection

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This paper cites Are we really making much progress? a worrying analysis of recent neural recommendation approaches.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Are we really making much progress? a worrying analysis of recent neural recommendation approaches

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This paper cites Attention is all you need.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Attention is all you need

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This paper cites Are Transformers Effective for Time Series Forecasting?.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Are Transformers Effective for Time Series Forecasting?

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This paper cites Long- term forecasting with TiDE: Time-series dense encoder.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Long- term forecasting with TiDE: Time-series dense encoder

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This paper cites Robust and unsupervised KPI anomaly detection based on conditional variational autoencoder.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Robust and unsupervised KPI anomaly detection based on conditional variational autoencoder

Reference 60

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This paper cites Anomaly detection in streams with extreme value theory.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Anomaly detection in streams with extreme value theory

Reference 61

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Observation 46ff2f37-2b50-4b9b-a854-6c3d36029b5b · outbound

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Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 62

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Observation 8e8f5135-7014-4213-8777-e05a52a33296 · outbound

This paper cites Soft-DTW: a Differentiable Loss Function for Time-Series.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Soft-DTW: a Differentiable Loss Function for Time-Series

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:51:08.406839Z

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Observation d07d1b77-7617-4393-8a29-6d398174462d · outbound

This paper cites Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Reference 64

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Observation 1f426541-bc2f-4e9a-b22f-646aee41a417 · outbound

This paper cites The advantages of the matthews correlation coefficient (MCC) over f1 score and accuracy in binary classification evaluation.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series The advantages of the matthews correlation coefficient (MCC) over f1 score and accuracy in binary classification evaluation

Reference 65

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Observation 5b036014-d507-4b4c-b278-b16591bd019e · outbound

This paper cites Dynamic programming algorithm optimization for spoken word recognition.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Dynamic programming algorithm optimization for spoken word recognition

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:10.669063Z

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Observation cf89a06d-96f1-44bb-872c-8aff6f2d5274 · outbound

This paper cites an unresolved cited work.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Unresolved cited work

Reference 67

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Observation 1a94f3c4-4df8-4c92-9988-e0f52a51195e · outbound

This paper cites A dynamic programming approach to continuous speech recognition.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series A dynamic programming approach to continuous speech recognition

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:10.681477Z

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Observation 8e7add58-f46c-4b23-bc9f-6a6a5ec1b1a3 · outbound

This paper cites Metric learning for temporal sequence alignment.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Metric learning for temporal sequence alignment

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:51:10.655319Z

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Observation dc67ed9c-53b4-4d94-8f7b-168fcbb54ab4 · outbound

This paper cites Fraud Dataset Benchmark and Applications.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series Fraud Dataset Benchmark and Applications

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T22:51:08.109686Z

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Observation 4dedbcae-09ac-45d1-95b3-1bc7717b2c88 · outbound

This paper cites URL https://www.sciencedirect.com/ science/article/pii/S0263224122000914.

Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series URL https://www.sciencedirect.com/ science/article/pii/S0263224122000914

Reference 2241

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