Deep Learning for Time Series Anomaly Detection: A Survey

Abstract

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

42 pages, 12 figures, 5 tables

References in corpus (31)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Transformers in Vision: A Survey
• Deep Learning for Anomaly Detection: A Review
• Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
• Deep Learning for Anomaly Detection: A Survey
• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
• Time-Series Anomaly Detection Service at Microsoft
• Deep Learning for Time Series Anomaly Detection: A Survey
• Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks
• Deep and Confident Prediction for Time Series at Uber
• LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
• An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
• Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
• Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art
• TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks
• Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
• Dilated Recurrent Neural Networks
• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
• Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering
• Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning
• Local Evaluation of Time Series Anomaly Detection Algorithms
• Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model
• Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
• Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version
• A Joint Model for IT Operation Series Prediction and Anomaly Detection
• Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
• VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
• Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series

Cited by in corpus (14)

• Deep Learning for Time Series Anomaly Detection: A Survey
• DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
• Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
• Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection
• Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems
• Quantum Autoencoder for Multivariate Time Series Anomaly Detection
• DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments
• An Unsupervised Deep Explainable AI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals
• Universal Domain Adaptation Benchmark for Time Series Data Representation
• Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey
• Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model
• Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
• Deep Joint Distribution Optimal Transport for Universal Domain Adaptation on Time Series
• Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection