Anomaly Detection in Time Series Data:Comprehensive Survey of Machine Learning and DeepLearning Approaches

Anomaly detection in time series data is a crucial task with wide-ranging applications in finance, healthcare, manufacturing, and cybersecurity. Detecting unusual patterns or behaviors in data streams is essential for ensuring system reliability, security, and efficiency. This paper presents a comprehensive survey of machine learning (ML) and deep learning (DL) techniques for anomaly detection in time series data. By examining state-of-the-art algorithms, our research aims to enhance the accuracy, robustness, and scalability of anomaly detection systems. We systematically review the strengths and limitations of existing methods, propose innovative approaches, and validate their performance through extensive experiments on various datasets. The findings from this study are anticipated to push anomaly detection forward, providing real-world solutions for tackling challenges in different fields and directing future research efforts.