Models for Time Series

implemented unsupervised anomaly detection models for time series data.

models.TimesNet

TIMESNET: Temporal 2D-Variation Modeling for General Time Series Analysis (ICLR'23)

models.DCdetector

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (KDD'23)

models.AnomalyTransformer

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR'22)

models.NCAD

Neural Contextual Anomaly Detection for Time Series.

models.TranAD

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (VLDB'22)

models.COUTA

Calibrated One-class classifier for Unsupervised Time series Anomaly detection (arXiv'22)

models.TcnED

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series (TNNLS'21)

models.DeepIsolationForestTS

Deep isolation forest for anomaly detection (TKDE'23)

models.DeepSVDDTS

Deep One-class Classification for Anomaly Detection (ICML'18)

models.DeepSADTS

Deep Semi-supervised Anomaly Detection (ICLR'20) []

models.DevNetTS

Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19) [BPSvdH19]

models.PReNetTS

Deep Weakly-supervised Anomaly Detection (KDD‘23)

References

[BPSvdH19] (1,2)

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.

[BRVG+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.