AN APPROACH TO EVALUATE THE QUALITY OF ANOMALY DETECTION IN TECHNOLOGICAL SIGNALS
A technological signal is a one-dimensional time series, which is an ordered sequence of discrete-time data. Due to the presence of time dimension, methods for detecting anomalies in time series should take into account time correlations and other time-related features. As a rule, the inaccuracy matrix and metrics derived from it, such as accuracy, completeness, F-measure, etc., are used to assess the quality of the anomaly detection method. These metrics, however, do not take into account the presence of time dimension. The metric, composed of the F-measure and distance-based metrics, allows you to take into account the moment of the beginning of the anomaly, the balance between errors of the first and second kind, the presence of an anomalous site. The paper proposes an approach to the construction of a quality metric for detecting anomalies in technological signals that comprehensively components these characteristics. Due to this arrangement, the metric evaluates the time dimension of the data, allows for a more adequate assessment of the presence of point anomalies and the occurrence of abnormal areas in technological signals, taking into account their characteristics, separating them from regular (normal, typical) signals. The integral assessment takes into account not only various features of the data, but can also be configured taking into account the specifics of a specific task. The paper presents the results of testing the proposed quality metric, which showed the effectiveness of the proposed approach to assessing the quality of anomaly detection in technological signals. The superiority of the proposed metric in the considered situations was on average more than 10%. At the same time, an additional key advantage of the metric over its analogues is the ability to configure it taking into account the specifics of the data and the models used for detecting anomalies.