Deep Learning for Time Series Anomaly Detection: A Survey

By Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi. ACM Computing Surveys, 2022

636 citations recorded by the source corpus.

Time series anomaly detection is important for a wide range of research fields and applications, including financial markets, economics, earth sciences, manufacturing, and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, and heart palpitations, and is therefore of particular interest. The large size and complexity of patterns in time series data have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey provides a structured and comprehensive overview of state-of-the-art deep learning for time series anomaly detection. It provides a taxonomy based on anomaly detection strategies and deep learning models. Aside from describing the basic anomaly detection techniques in each category, their 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. Finally, it summarises open issues in research and challenges faced while adopting deep anomaly detection models to time series data.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b9830d64bb2cf35197b2597b030da05f0fe66dea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ming Jin
2. Liang Sun
3. Tian Zhou
4. Geoffrey I. Webb
5. Cesare Alippi
6. Yiyuan Yang
7. Chaoli Zhang
8. Shirui Pan

In Computer Networks and Communications

1. Geoffrey I. Webb
2. Cesare Alippi
3. Liang Sun
4. Shirui Pan
5. Qingsong Wen
6. Tian Zhou

In Signal Processing

1. Tian Zhou
2. Liang Sun
3. Geoffrey I. Webb
4. Cesare Alippi
5. Yiyuan Yang
6. Chaoli Zhang
7. Shirui Pan
8. Qingsong Wen

Citations in the corpus

• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, methodology): “Unsupervised methods are popular without strict requirements on labeled data, including one class classification-based, probabilisticbased, distance-based, forecasting-based, reconstruction-based approaches [11, 24, 42, 59, 66, 85, 92].”
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “Advancements in the anomaly detection and diagnosis field have led to the proposal of more comprehensive backbone and scoring modules [136], [145], primarily driven by the adoption of GNN methodologies [35], [36], [56].”