Abstract
Given the exponential growth of data in various sectors, the timely identification of anomalies has become crucial in mitigating risks such as financial losses and data breaches. This research focuses on advancing multivariate time series anomaly detection through the innovative application of neuro-evolutionary methods. Utilizing the Evolutionary eXploration of Augmenting Memory Models (EXAMM) algorithm, the study automates the design and optimization of Long Short-Term Memory (LSTM) autoencoder networks, enabling enhanced forecasting capabilities in recurrent neural network architectures. Leveraging these networks along with a One-Class Support Vector Machine (OCSVM) to generate boundary thresholds, this project delivers a robust and scalable procedure for identifying anomalies that outperforms existing proven methods in terms of accuracy and cumulative F1 score, while at the same time requiring orders of magnitude fewer trainable parameters. By comparing the reconstruction error of trained forecasts, the proposed approach facilitates accurate anomaly detection across diverse datasets against unseen anomalous data and informs efficient design decisions for future autoencoder network architectures.
Library of Congress Subject Headings
Anomaly detection (Computer security); Multivariate analysis--Data processing
Publication Date
2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Computer Science (MS)
Department, Program, or Center
Computer Science, Department of
College
Golisano College of Computing and Information Sciences
Advisor
Alexander Ororbia
Advisor/Committee Member
Travis Desell
Advisor/Committee Member
Rui Li
Recommended Citation
Jha, Aryan, "Neuro-Evolution for Multivariate Time Series Anomaly Detection" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12030
Campus
RIT – Main Campus
Plan Codes
COMPSCI-MS