The built-in methods

Statistical Methods

Class Name	Requirements	Description	Ref.
MatrixProfile	matrixprofile(pypi)		Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds
SubLOF	sklearn	LOF in sequence manner	LOF: identifying density-based local outliers
SAND	tslearn==0.4.1		SAND: streaming subsequence anomaly detection
SubOCSVM	sklearn	OCSVM in sequence manner	Support Vector Method for Novelty Detection

Prediction-based

Class Name	Requirements	Description	Ref.
AR	pytorch	AutoRegression implemented by a torch linear (using first order difference)	Robust regression and outlier detection
LSTMADalpha	pytorch	LSTMAD in a seq2seq manner	Long Short Term Memory Networks for Anomaly Detection in Time Series
LSTMADbeta	pytorch	LSTMAD in a multi-step prediction manner	Long Short Term Memory Networks for Anomaly Detection in Time Series

Reconstruction-based

Class Name	Requirements	Description	Ref.
AE	pytorch	AutoEncoder	Sparse autoencoder
EncDecAD	pytorch	Combine LSTM and AE	LSTM-based encoder-decoder for multi- sensor anomaly detection
SRCNN	pytorch		Time-series anomaly detection service at microsoft
Amomaly Transformer	pytorch		Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
TFAD	pytorch-lightning		TFAD: A decomposition time series anomaly detection architecture with time-frequency analysis

VAE-based

Class Name	Requirements	Description	Ref.
Donut	pytorch	Unsupervised anomaly 1032 detection via variational auto-encoder for seasonal kpis in web applications
FCVAE	pytorch-lightning	Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

Representation Learning

Class Name	Requirements	Description	Ref.
DCDetector	pytorch		DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

General TimeSeries Models

Class Name	Requirements	Description	Ref.
TimesNet	pytorch		TIMESNET: TEMPORAL 2D-VARIATION MODELING FOR GENERAL TIME SERIES ANALYSIS
OFA	pytorch	One Fits all, freezing some GPT2 params	One Fits All: Power General Time Series Analysis by Pretrained LM
FITS	pytorch		FITS: Modeling Time Series with $10K$ Parameters