Anomaly detection on multivariate time-series

List of papers & datasets for anomaly detection on multivariate time-series data.

Contents

• 1. Papers
• 2. Books
• 3. Datasets
• 4. Evaluate
  • 4.1 Metrics
  • 4.2 Threshold
    • 4.2.1 Label-Based Threshold Search
    • 4.2.2 Thresholds Search without labels
• 5. Point Adjust & Point Adjust %K & Original

1. Papers

Name	Code	Key word	Published
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data	MSCRED	CH2	AAAI'19
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series	GDN	CH2	AAAI'21
Multivariate Time-series Anomaly Detection via Graph Attention Network	MTAD_GAT	CH2	ICDM'20
USAD : UnSupervised Anomaly Detection on Multivariate Time Series	USAD	adversarial	KDD'20
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks	MAD_GAN		ICANN'19
Robust anomaly detection for multivariate time series through stochastic recurrent neural network	OmniAnomaly		KDD'19
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	DAGMM		ICLR'18
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data	TranAD		VLDB'22
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy	Anomaly Transformer		ICLR'22
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network Lifeng	THOC(None)		NeurIPS'20
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals	CAE-M(None)		TKDE'21
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT	GTA		IoTJ'21
Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding	InterFusion		KDD'21
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding	LSTM-NDT		KDD'18
Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering	NSIBF		KDD'21

2. Books

• Outlier Analysis
• Introduction to Time Series and Forecasting, Second Edition
• Anomaly Detection Principles and Algorithms
• Outlier Ensembles An Introduction

3. Datasets

• SWaT & WaDI: SWaT Dataset Download, SWaT Dataset Introduce, WaDI Dataset Introduce | data_preprocess
• MSL & SMAP: Dataset Download and Introduction | data_preprocess
• SMD: Dataset Download and Introduction | data_preprocess
• ASD: Dataset Download and Introduction
• PSM: Dataset Download and Introduction
• KDDCup99: Dataset Download and Introduction
• MSDS: Dataset Download and Introduction
• MIT-BIH: Dataset Download and Introduction
• KDDCup21: Dataset Download and Introduction
• Wind Turbines: Dataset Download and Introduction
• Others:
  • Numenta's NAB
  • Yahoo's Webscope S5---Download Harder
  • 2020 Skoltech's SKAB
  • 2018 AIOps's KPI-Anomaly-Detection

4. Evaluate

4.1 Metrics

Ground truth	Predict	Predict
	Abnormal	Normal
Abnormal	TP	FN
Normal	FP	TN

• Precision: $P=\frac{TP}{TP+FP}$

• Recall: $R=\frac{TP}{TP+FN}$

• F1: $F1=\frac{2\times P\times R}{P+R}$

• AUC: $\mathrm{TPR}=\frac{TP}{TP+FN}$ $\mathrm{FPR}=\frac{FP}{TN+FP}$

4.2 Threshold

4.2.1 Label-Based Threshold Search

• Best F1

4.2.2 Thresholds Search without labels

• $Val_{max}(Train_{max})$ F1

3 sigma rule： $Val_{max}(Train_{max}) \approx mean + 3 \times std$

• Pot F1
• Epsilon F1

5. Point Adjust & Point Adjust %K & Original

• Point Adjust