A Review on Outlier/Anomaly Detection in Time Series Data

By Ane Blázquez-García, Angel Conde, Usue Mori, José Antonio Lozano. ACM Computing Surveys, 2020

1,061 citations recorded by the source corpus.

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

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

View Paper: https://www.semanticscholar.org/paper/429173b6033ce240e0b5930379417f703d0e38fe

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Citations in the corpus

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “The analysis of time series not only provides insights into past trends but also facilitates a multitude of tasks such as forecasting [4], classiﬁcation [5], anomaly detection [6], and data imputation [7].”
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (2020, background): “We refer the interested reader to [10] which offers the reader a detailed review and taxonomy.”
Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “Although the field of anomaly detection has been explored in several literature surveys [140], [27], [20], [23] and some evaluation review papers exist [154], [102], there is only one survey on deep anomaly detection methods for time series data [41].”
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT (2021, neutral): “Traditionally, the anomaly detection frameworks included two main phases: estimation phase and detection phase [28].”
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021, background): “To the best of our knowledge, previous reviews [1], [2], [9]–[14] on this subject matter do no more than simply categorize models according to their mechanisms and describe their characteristics.”
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (2021, background): “A comprehensive summary is given in (Bl´azquez-Garc´ıa et al. 2020).”
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, background): “classified as statistical, classic machine learning, and deep learningbased methods [6, 56].”