A Modeling Method for Time Series in Complex Industrial System

The data of complex industrial system were usually arrayed in the form of time series. This paper put forward the multivariate time-delayed principal component regression (MTPCR) method, which utilized the historical time series in the production process so as to establish a systematic prediction model. This method can calculate the delayed time of each input and output tunnel by which the modeling data were selected. The model established can predict the production outcome and product quality accurately in accordance with real-time input. With the aid of Simulink data and Matlab arithmetic, this paper concludes that MTPCR method possesses higher precision compared with other method