Anomaly Detection Algorithm Using the SARIMA Model for an Automated Complex for Aquatic Environment Biomonitoring
Journal: Scientific and Technical Information Processing
Year: 2025

The paper presents an algorithm for anomaly detection in bivalve activity data using the error between the predicted value using the SARIMA model and the actual value. Decomposition of time series was carried out to determine the seasonal component of the models. An optimal model for all averaging times of the activity data of freshwater bivalve was made. After this, using the developed algorithmic software, the RMSE metric was calculated for the entire data set, which made it possible to determ