False in Season Anomaly Suppression

False in-season anomaly suppression focuses on improving the accuracy of anomaly detection in time series data, particularly by mitigating false positives arising from data points that fall within expected ranges but deviate slightly from typical patterns. Current research emphasizes unsupervised and hybrid approaches combining change-point detection with statistical process control or ensemble methods like those using multiple encoders and decoders, often incorporating LSTM networks for sequential data processing. These advancements aim to enhance the reliability of anomaly detection across diverse applications, from power grid monitoring and critical infrastructure management to bio-regenerative life support systems and healthcare, ultimately leading to more informed decision-making and improved system safety.