Author ORCID Identifier

https://orcid.org/0009-0005-4016-2666

Semester

Spring

Date of Graduation

2024

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Industrial and Managements Systems Engineering

Committee Chair

Imtiaz Ahmed

Committee Member

Srinjoy Das

Committee Member

Zhichao Liu

Abstract

In this study, we propose a novel anomaly detection framework designed specifically for Multivariate Time Series (MTS) data, addressing the prevalent challenges in analyzing such complex datasets. The detection of anomalies within MTS data is notably difficult due to the complex interplay of numerous variables, temporal dependencies, and the common issue of class imbalance, where one category significantly outnumbers another. Traditional deep learning (DL) approaches often fall short in simultaneously tackling these issues. Our framework is designed to address these challenges through a two-phased approach. Phase I employs Conditional Tabular Generative Adversarial Networks (CTGAN) to create strategic synthetic data, setting the stage for Phase II, which utilizes a hybrid DL architecture. This architecture combines Gated Recurrent Units (GRU), Temporal Convolutional Networks (TCN), and an Attention Mechanism, significantly improving the detection of anomalies. Our approach is tailored to overcome the hurdles of class imbalance — using strategic data augmentation in Phase I — and to address the intricacies of variable interactions and long-term temporal dependencies through a hybrid DL model in Phase II. The efficacy of our framework is demonstrated through the Controlled Anomaly Time Series (CATS) dataset, notable for its complexity with over 5 million timestamps, 17 features, and a marked class imbalance. Our methodology distinguishes itself by detecting subtle anomalies, capturing long-range dependencies more effectively, and enhancing interpretability through the visualization of attention weights. Furthermore, our anomaly detection framework is both scalable and adaptable across different domains, marking a considerable improvement over existing methods. A performance comparison with other models, including standalone GRU, TCN, combined GRU-TCN, and GRU-TCN with Attention, showcases the superior capability of our framework, particularly in managing the intricacies and rarity of anomalies in the CATS dataset. This framework not only addresses the challenges of data imbalance and complexity inherent in MTS datasets but also harnesses the strengths of various DL architectures to provide an effective anomaly detection solution. Our contribution promises significant advancements in the accuracy, reliability, and interpretability of anomaly detection models, representing a major leap forward in this domain.

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