Anomaly Detection Algorithm Using the SARIMA Model for an Automated Complex for Aquatic Environment Biomonitoring

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 determine the potential threshold for the operation of the algorithm, as well as the algorithm’s response time to anomalies at different data averaging times. The results will be included in the algorithmic software of an automated complex for biomonitoring the state of water quality based on bivalves, which is already functioning and located in the waters of Sevastopol, which will allow more rapid and more likely detection of anomalies and generate an alarm signal.