Extreme Events Characterization on Time Series - Rodrigues, Marcos Wander; Zárate, Luis Enrique (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2020: Anais do VIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2020.11959, 2020)
The use of sensors in environments where they require constant monitoring has been increasing in recent years. The main goal is to guarantee the effectiveness, safety, and smooth functioning of the system. To identify the occurrence of abnormal events, we propose a methodology that aims to detect patterns that can lead to abrupt changes in the behavior of the sensor signals. To achieve this objective, we provide a strategy to characterize the time series, and we use a clustering technique to analyze the temporal evolution of the sensor system. To validate our methodology, we propose the clusters’ stability index by windowing. Also, we have developed a parameterizable time series generator, which allows us to represent different operational scenarios for a sensor system where extreme anomalies may arise.