ANOMALY DETECTION IN TIME SERIES DATA: TRENDS, APPLICATIONS, AND RESEARCH GAPS

Abstract

Time series anomaly detection plays a pivotal role in modern intelligent systems, enabling the identification of irregular patterns within sequential data across domains such as finance, healthcare, manufacturing, and cybersecurity. With the rapid proliferation of IoT devices and sensors generating massive real-time data streams, detecting anomalies has become increasingly critical yet complex. This review comprehensively examines recent advancements in anomaly detection techniques, encompassing statistical models, traditional machine learning algorithms, and emerging deep learning architectures. It contrasts classical methods such as ARIMA and STL decomposition with advanced models like RNNs, LSTMs, autoencoders, and transformer-based frameworks, emphasizing their comparative strengths in scalability, interpretability, and performance. Additionally, the review highlights cross-domain applications, including fraud detection, predictive maintenance, disease surveillance, and environmental monitoring, demonstrating the widespread utility of anomaly detection systems. Emerging trends such as explainable AI, federated and edge learning, and privacy-preserving frameworks are explored as key enablers of trustworthy and adaptive solutions. The study also identifies research gaps, including the scarcity of benchmark datasets, challenges in handling high-dimensional and multivariate data, and issues of generalizability across domains. By consolidating existing literature, this review proposes a taxonomy of techniques and outlines future directions for developing transparent, efficient, and domain-agnostic anomaly detection frameworks.