Deep Learning for Anomaly Detection in Time-Series Data: An Analysis of Techniques, Review of Applications, and Guidelines for Future Research

Industries are generating massive amounts of data due to increased automation and interconnectedness. As data from various sources becomes more available, the extraction of relevant information becomes crucial for understanding complex systems' behavior and performance. The growing volume and complexity of time-series data in diverse industries have created a demand for effective anomaly detection methods. Detecting anomalies in multivariate time-series data presents unique challenges, such as the presence of multiple correlated variables and intricate relationships among them. Traditional approaches often fall short in detecting anomalies, making deep learning methods a promising solution. This review article provides a comprehensive analysis of different deep learning techniques for anomaly detection in time-series data, examining their applicability across various industries and discussing the associated challenges. The article emphasizes the significance of deep learning in detecting anomalies and offers valuable insights to inform decision-making processes. Furthermore, it proposes recommendations for model developers, advocating for the development of hybrid models that combine different deep learning techniques and the exploration of attention mechanisms in Recurrent Neural Networks (RNNs). These recommendations aim to overcome the challenges associated with deep learning-based anomaly detection in multivariate time-series data.