Abstract Predictive analytics is a field of knowledge that allows you to make informed decisions, prepare for unforeseen situations and anticipate all kinds of emergencies. Recently, predictive analysis has been actively used in industry: based on historical data, the model makes a probabilistic forecast of the device’s behavior in the near future. This paper provides a comparative analysis of two predictive models, which both could self-learn and had the property of self-correction. The accuracy of predicting the development of a defect in industrial equipment, as well as the prediction horizon, were evaluated. Particular attention is paid to the peculiarity of working with data obtained from production sensors.