A model for STIRPAT-based fusion predictive analytics

In this paper, a model for STIRPAT-based fusion predictive analytics is proposed by building ARIMA time series model, BCC model, data envelopment analysis model, system dynamics model and STIRPAT model. The model solves the problem of predicting and analyzing heterogeneous data. This paper uses data such as IHSMarkit analysis reports and related patents to confirm the validity of the model. Finally, the model successfully predicts the time to achieve carbon neutrality in the Yangtze River Delta region and the amount of automobile ownership.