Real-Time Outlier Detection in Fast-Moving Data Streams - Eka Puji Agustini; Mohd Zaki Zakaria (International Journal of Advances in Artificial Intelligence and Machine Learning, 2024)
Anomaly detection is a critical task in various fields such as finance, healthcare, network monitoring, and sensor data analysis, where identifying unusual patterns or outliers in data streams is essential for timely decision-making. Two commonly used techniques for anomaly detection are the Moving Average (MA) and Exponential Moving Average (EMA) methods. Despite their widespread use, selecting the appropriate method depends on the nature of the data and the requirements of the system. This paper presents a comparative analysis of MA and EMA for anomaly detection, focusing on critical factors such as speed of detection, stability, precision and recall, false positive rate, and computational efficiency. This research addresses the problem of determining which method, MA or EMA, is better suited for specific types of data, particularly in streaming environments with varying trends and anomalies. The results of our comparison indicate that EMA performs better in dynamic environments where rapid identification of anomalies is critical, such as financial markets or network traffic analysis. It quickly detects sudden deviations but may flag minor fluctuations as false positives due to its sensitivity. MA, on the other hand, is more stable and computationally efficient, with a lower false positive rate, making it more suitable for applications where long-term trend monitoring is required, and stability is prioritized over speed. This research highlights the strengths and weaknesses of both methods, demonstrating that the choice between MA and EMA should be based on the specific needs of the anomaly detection system. For real-time, high-speed environments, EMA offers a more responsive solution, while MA provides better stability and efficiency in long-term monitoring. A hybrid approach combining both methods could offer a more robust solution, adapting to different types of data and detection requirements.