Anomaly Detection in Time Series

Borges, Heraldo; Akbarinia, Reza; Masséglia, Florent

doi:10.1007/978-3-662-64553-6_3

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…While analyzing an MTS dataset of ICS, we have to care for the outliers–abnormal observations collected from sensors and actuators during industrial processes that are predicted as unwanted and deviated from the usual and expected behaviors [ 21 , 22 ]. Generally, there are two main approaches for detecting anomalies in the MTS dataset of ICS.…”

Section: Anomaly Detection In Industrial Control Systemsmentioning

confidence: 99%

A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems

Kim

Alawami

Kim

et al. 2023

Sensors

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Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS. However, most models have been implemented and evaluated under specific circumstances, which leads to confusion about choosing the best model in a real-world situation. In other words, there still needs to be a comprehensive comparison of state-of-the-art anomaly detection models with common experimental configurations. To address this problem, we conduct a comparative study of five representative time series anomaly detection models: InterFusion, RANSynCoder, GDN, LSTM-ED, and USAD. We specifically compare the performance analysis of the models in detection accuracy, training, and testing times with two publicly available datasets: SWaT and HAI. The experimental results show that the best model results are inconsistent with the datasets. For SWaT, InterFusion achieves the highest F1-score of 90.7% while RANSynCoder achieves the highest F1-score of 82.9% for HAI. We also investigate the effects of the training set size on the performance of anomaly detection models. We found that about 40% of the entire training set would be sufficient to build a model producing a similar performance compared to using the entire training set.

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Section: Anomaly Detection In Industrial Control Systemsmentioning

confidence: 99%

A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems

Kim

Alawami

Kim

et al. 2023

Sensors

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“…An outlier detection method can be applied to multiple data types such as images, transaction data, sequence data including genomics and time series. There are two main types of anomaly detection, point wise and pattern also called collective anomalies or discords [3]. Discords denote the most unusual time series sub sequences, and are detected using similarity measures that compare sub sequences with each other for a given length.…”

Section: Introductionmentioning

confidence: 99%

On the Ranking of Variable Length Discords Through a Hybrid Outlier Detection Approach

Khansa¹,

Gervet²,

Brouillet³

2022

Lecture Notes in Computer Science

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In this paper we are interested in identifying insightful changes in climate observations series, through outlier detection techniques. Discords are outliers that cover a certain length instead of being a single point in the time series. The choice of the length can be critical, leading to works on computing variable length discords. This increases the number of discords, with potential overlapping, subsumption and reduced insightful results. In this work we introduce a hybrid approach to rank variable length discords and extract the most prominent ones, that can yield more impactful results. We propose a ranking function over extracted variable length discords that accounts for contained point anomalies. We investigate the combination of pattern wise anomaly detection, through the Matrix Profile paradigm, with two different point wise anomaly detectors. We experimented with MAD and PROPHET algorithms based on different concepts to extract point anomalies. We tested our approach on climate observations, representing monthly runoff time series between 1902 and 2005 over the West African region. Experimental results indicate that PROPHET combined with the Matrix Profile method, yields more qualitative rankings, through an extraction of higher values of extreme events within the variable length discords.

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Comparative Investigation of Machine Learning and Deep Learning Methods for Univariate AQI Forecasting

Rizwan,

Patankar

2023

Lecture Notes in Networks and Systems

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Anomaly Detection in Time Series

Cited by 6 publications

References 26 publications

A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems

A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems

On the Ranking of Variable Length Discords Through a Hybrid Outlier Detection Approach

Comparative Investigation of Machine Learning and Deep Learning Methods for Univariate AQI Forecasting

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