Abstract: Anomaly detection in time series data is a critical task with numerous applications across various domains. This study presents a comprehensive empirical review that explores the methodologies, evaluation metrics, benchmark datasets, novel techniques, and real-world case studies in anomaly detection. The study begins by examining the different methodologies employed in anomaly detection, including statistical-based methods, machine learning-based approaches, and deep learning-based techniques. Evaluation metrics such as precision, Recall, F1-score, ROC curve, and AUC are discussed, along with commonly used benchmark datasets that serve as standards for evaluating anomaly detection algorithms. Novel techniques and algorithms for anomaly detection in time series data are critically analyzed, including time series decomposition and reconstruction methods, transfer learning and domain adaptation techniques, online and streaming anomaly detection approaches, and ensemble methods. These techniques' strengths, limitations, and potential applications are discussed in detail.

Real-world case studies showcase the practical applications of anomaly detection in different domains. These case studies include anomaly detection in network traffic data, energy consumption patterns, and medical sensor data. The mathematical approaches and algorithms employed in these case studies are examined to provide insights into the specific methodologies used for anomaly detection. Future research directions and challenges in anomaly detection are discussed, highlighting the importance of explainable AI and interpretable models, incorporating domain knowledge and context awareness, privacy-preserving techniques, and integrating anomaly detection with other data analysis techniques. This empirical review contributes to the field by comprehensively analyzing anomaly detection techniques in time series data. The study's findings offer valuable insights into the strengths and limitations of different methodologies and highlight emerging trends in the field. Furthermore, future research directions and challenges provide a roadmap for advancing anomaly detection in time series data analysis.

Keywords: anomaly detection; time series; cybersecurity; neural network; machine learning

PDF | DOI: 10.17148/IARJSET.2023.10507

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