An Enhancing Timeseries Anomaly Detection Using LSTM and Bi-LSTM Architectures

Abstract

Anomaly detection in time series data plays a critical role in various domains, including cybersecurity, industrial monitoring, and financial fraud detection. In recent years, deep learning models have emerged as promising tools for addressing the challenges associated with anomaly detection in sequential data. This study investigates the effectiveness of advanced deep learning models in detecting anomalies within real-world time series datasets. Utilizing a diverse dataset, which encompasses a diverse range of time series data, we evaluate the performance of these models across multiple domains. Our experimental results demonstrate the high accuracy and robustness of the evaluated models in detecting anomalies. These models showcase the ability to capture complex temporal patterns and discern anomalies effectively, thereby showcasing their potential for real-world applications. Additionally, we discuss future research directions, emphasizing the exploration of alternative approaches for real-time anomaly detection and the optimization of models for deployment in production environments.