Advances in Deep Learning for Multivariate Time-Series Anomaly Detection

Anomaly detection in multivariate time series data is essential across a range of industries, including finance, healthcare, and manufacturing, where timely identification of unusual patterns can prevent significant operational disruptions. This study explores the applications of deep learning methods including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Autoencoders, Hybrid models, and Transformers in time series anomaly detection. By analyzing data collected from various sensors that monitor key parameters such as temperature, pressure, vibration, and sound, this research addresses the complexities associated with detecting deviations from normal behavior in high-dimensional datasets. We deeply analyze the methodologies, datasets, and evaluation metrics employed in existing studies, focusing on the comparative performance of these techniques. This review also highlights the strengths and weaknesses of each approach, providing a detailed understanding of their applicability to different types of time series data. By synthesizing insights from numerous research contributions, we aim to chart the current landscape of deep learning-based anomaly detection, uncover key challenges, and suggest potential directions for future research in this rapidly evolving field.