Anomaly Detection

Problem Description

Time Series Anomaly Detection’s task is to find out the possible anomalies lies in time series, like this:

To formalize, give a time series with either single channel or multi channels:

We aim to find the possible anomalies lies in X:

Models

Abbr	Algorithm	type	supervise	Year	Class	Ref
IForest	Isolation forest.	outlier ensembles	unsupervised	2008	realseries.models.iforest.IForest	[IForest]
LSTM_dynamic	LSTM based nonparametricanomaly thresholding.	neural networks	unsupervised	2018	realseries.models.lstm_dynamic.LSTM_dynamic	[LSTM_dynamic]
Lumino	A light weight library luminol.	distance based	unsupervised	2017	realseries.models.lumino.Lumino	[Lumino]
RCForest	Robust random cut forest.	outlier ensembles	unsupervised	2016	realseries.models.rcforest.RCForest	[RCForest]
LSTMED	RNN based time-series prediction.	neural networks	unsupervised	2016	realseries.models.rnn.LSTMED	[LSTMED]
SR_CNN	Spectral Residual and ConvNet based anomaly detection.	neural networks	supervised	2019	realseries.models.srcnn.SR_CNN	[SR_CNN]
STL	Seasonal and Trend decomposition using Loess.	weighted regression	unsupervised	1990	realseries.models.stl.STL	[STL]
VAE	Variational Auto-Encoder anomaly detection.	probabilistic	unsupervised	2018	realseries.models.vae_ad.VAE_AD	[VAE]

References

IForest

Liu F T, Ting K M, Zhou Z H. Isolation forest[C]//2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008: 413-422.

LSTM_dynamic

Hundman K, Constantinou V, Laporte C, et al. Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 387-395.

Lumino

Luminol. Github, https://github.com/linkedin/luminol

RCForest

19. Guha, N. Mishra, G. Roy, & O. Schrijvers, Robust random cut forest based anomaly detection on streams, in Proceedings of the 33rd International conference on machine learning, New York, NY, 2016 (pp. 2712-2721).

LSTMED

Malhotra P, Ramakrishnan A, Anand G, et al. LSTM-based encoder-decoder for multi-sensor anomaly detection[J]. arXiv preprint arXiv:1607.00148, 2016.

SR_CNN

Ren H, Xu B, Wang Y, et al. Time-Series Anomaly Detection Service at Microsoft[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 3009-3017.

STL

Cleveland R B. STL: A seasonal-trend decomposition procedure based on loess. 1990[J]. DOI: citeulike-article-id, 1435502.

VAE

Xu H, Chen W, Zhao N, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications[C]//Proceedings of the 2018 World Wide Web Conference. 2018: 187-196.

Robust

Su Y, Zhao Y, Niu C, et al. Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019: 2828-2837.

NAB

Ahmad S, Lavin A, Purdy S, et al. Unsupervised real-time anomaly detection for streaming data[J]. Neurocomputing, 2017, 262: 134-147.

NAB2

Lavin A, Ahmad S. Evaluating Real-Time Anomaly Detection Algorithms–The Numenta Anomaly Benchmark[C]//2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015: 38-44.