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Chung-Ang University Researchers Review Deep Learning-Based Methods to Detect Time Series Data Anomaly

Researchers analyze state-of-the-art approaches, limitations, and applications of deep learning-based anomaly detection in multivariate time series

Unusual observations or anomalies in recorded data are common. Detection of such anomalies has applications in identifying credit card frauds, industrial intrusion, weather changes, medical seizures, etc. Chung-Ang University researchers have now provided an evaluation of deep learning-based methods for anomaly detection in multivariate time series, their applications, and the challenges involved. The study could help researchers take stock of future research directions related to anomaly detection.

Title: NDVI time series showing data from two sources.

Caption: In a recent study, a research team from Chung-Ang University, Korea presents open research questions related to anomaly detection using deep learning and curates open-access time series datasets, an invaluable asset for selecting the appropriate technique for a particular scientific or industrial problem and developing efficient anomaly detection techniques.

Image credit: Haoyang Xu from flickr

Image link: https://www.flickr.com/photos/15733096@N00/2722378670 

License type: CC-NC-SA 2.0

Monitoring financial security, industrial safety, medical conditions, climate, and pollution require analysis of large volumes of time series data. A crucial step in this analysis involves identification of unusual points, patterns, or events that deviate from a dataset. This is known as “anomaly detection” and is performed using data mining techniques. Although deep learning methods have been extensively applied in anomaly detection, there is no one-size-fits-all technique that works for multiple applications across a variety of fields. Further, existing studies on anomaly detection for multivariate time series focus solely on the approach without examining its challenges.

A group of researchers from Chung-Ang University in Korea have now addressed this gap by summarizing the applications based on anomaly detection. The team, including Professor Jason J. Jung and Dr. Gen Li, evaluated the current state-of-the-art anomaly detection techniques and addressed the challenges associated with them. Their work was made available online on October 17, 2022 and was published in Volume 91 of the journal Information Fusion on March 2023. “Our fundamental research topic is anomaly detection in multivariate time series. In this review, we have summarized the approaches, challenges, and applications for the same,” [A1] explains Prof. Jung. The researcher duo has worked extensively on time series anomaly detection for multiple variables and has previously published their works on seizure detection, climate monitoring and financial fraud monitoring that culminated in this review.

The team first classified the anomalies into three types, namely abnormal time points, time intervals, and time series. Next, they highlighted that, among the deep learning-based artificial neural networks, long short-term memory (LSTM) and autoencoders are most commonly used for detecting abnormal time points and time intervals. Additionally, they discussed alternative methods such as dynamic graphs that examine relational features between the time series and detect abnormal time intervals. An in-depth summary of the current limitations of the prevalent techniques emphasizing the root cause of anomalies was also provided.

Finally, the duo presented a thorough overview of the applications for anomaly detection in multivariate time series. They curated open-access time series datasets and also discussed the open research questions and challenges related to anomaly detection in multivariate time series.

The potential of deep learning-based approaches for anomaly detection is far-reaching, as Prof. Jung surmises, “I believe that this review will help researchers find the appropriate approach for detecting anomalies in their respective areas of work. For example, in the field of science, people can easily find out the open access datasets and the corresponding state-of-art anomaly detection method in this paper. For industrial applications, the appropriate anomaly detection techniques to identify damages and faults could be conveniently found in this review[A2] ”.

As for the challenges involved, developing a model for explaining the anomalies detected is of considerable worth since it can help us understand why the anomaly occurred in the first place. “The challenge is to identify the relationship between an abnormal time point and the time point leading to that anomaly,[A3] ” says Prof. Jung.

Taken together, this review is an invaluable resource for selecting appropriate anomaly detection techniques for various fields, as well as for developing more efficient anomaly detection techniques.

Reference

Additional information for EurekAlert

Latest Article Publication Date: March 2023

Method of Research: Modeling

Subject of Research: Not Applicable

Conflict of Interest Statement: The authors have no known conflict of interest to disclose.

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About Chung-Ang University

Chung-Ang University is a private comprehensive research university located in Seoul, South Korea. It was started as a kindergarten in 1916 and attained university status in 1953. It is fully accredited by the Ministry of Education of Korea. Chung-Ang University conducts research activities under the slogan of “Justice and Truth.” Its new vision for completing 100 years is “The Global Creative Leader.” Chung-Ang University offers undergraduate, postgraduate, and doctoral programs, which encompass a law school, management program, and medical school; it has 16 undergraduate and graduate schools each. Chung-Ang University’s culture and arts programs are considered the best in Korea.

Website: https://neweng.cau.ac.kr/index.do

About Professor Jason J. Jung

Jason J. Jung is a professor and Dean of School of Software at Chung-Ang University (CAU), South Korea. Before joining CAU, he was an Assistant Professor at the Yeungnam University, South Korea. He worked as a postdoctoral researcher in INRIA Rhone-Alpes, France, and a visiting scientist in Fraunhofer Institute (FIRST) in Berlin, Germany. Prof. Jung received BE in Computer Science and Mechanical Engineering, MS and PhD degrees in Computer and Information Engineering from Inha University. His research topics are knowledge engineering on social networks by using AI methodologies. He has authored over 450 articles and conference presentations and has garnered over 7000 citations.