Predictive models for metrological data of engineering systems

Abstract

Paper is devoted to the predictive models for metrological indicators on the real estate engineering infrastructure. The solution is in demand among many enterprises both in terms of security and economic considerations. The key task is to build a mathematical model performing predictions on the real data samples. We study both classical predictive models (ARIMA, SARIMA) and modern machine learning based approaches (RBF, LSTM), and compare them.