Instruments and Systems: Monitoring, Control, and Diagnostics Annotation << Back Cognitive Analysis Methods for Detecting Anomalies in Time Series in Cyberphysical Systems A.A. VOLKOV, D.A. PARSHIN The task of detecting anomalies is one of the key tasks in creating cyber-physical systems, as it allows you to analyze data coming from various devices in real time and assess the state of the environment. This article compares 14 one-dimensional methods for detecting anomalies in time series using a statistical approach and machine learning methods. This work will allow us to evaluate different approaches to the problem of detecting anomalies. The comparison of these methods is carried out on test data sets from open sources. The result of the work is an analysis of the accuracy and performance of methods for detecting anomalies in one-dimensional numerical series. As a result, statistical methods are more accurate, detecting point and collective anomalies, while requiring less time for calculations. The measurements given in this paper were performed on one-dimensional time series, and the detection of anomalies in multidimensional time series will be the subject of further study. Keywords: machine learning; smart home; time series; statistics; anomaly; data set; AUC ROC; performance; autoregression; exponential smoothing; gradient boosting; SVM. DOI: 10.25791/pribor.4.2021.1255 Pp. 42-49.