Benchmarking Outlier Detection: Integrating Classical Methods and Deep Learning Techniques for Advanced Fault Analysis

Outlier detection is a critical task in various domains, and the choice of detection method can significantly impact the accuracy and reliability of results. The choice of detection method can significantly impact the accuracy and reliability of results. This article presents a comprehensive comparative study of traditional outlier detection methods and deep learning approaches. The study evaluates the performance of classical techniques, such as spatiotemporal. And deep learning-based methods, including neural networks. Using a benchmark dataset. Finally, the article provides a practical application on real data concerning animal trajectories, where ST-DBSCAN is used along with the baseline model to predict the membership of outliers.