REMD: A Novel Hybrid Anomaly Detection Method Based on EMD and ARIMA

Anomalies are defined as behavioral deviations from expected patterns and pose challenges to identify them. Anomaly detection is a fundamental activity of time series analysis. It enables informed decision-making in many control and monitoring activities, such as healthcare, water quality, seismic reflection analysis, and oil exploration. Many anomaly detection methods exist, but choosing the appropriate methods is complex due to the intrinsic nature of the time series. There is a demand for robust and adaptable anomaly detection methods. This paper introduces Refined Empirical Mode Decomposition (REMD) as a hybrid approach addressing this need, integrating Empirical Mode Decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) models. REMD's design aims to optimize the strengths of both methods and overcome their limitations. It is evaluated against state-of-the-art methods on diverse datasets. It demonstrates superior performance, with up to three times better F1 score.