Forecasting of throughput performance using an ARMA model with improved differential evolution algorithm

In this paper, in order to investigate throughput performance of each unit passed by electroplating products in cold rolling stage of iron and steel enterprise, the autoregressive moving average (ARMA) model is developed to forecast the throughput of each unit. In addition, an improved differential evolution (IDE) algorithm is employed to optimize parameters of the developed model. Finally, the actual production data of an iron and steel enterprise is used for carrying out experiments. The results show that the developed ARMA forecast models can forecast the throughput of each unit with acceptable prediction accuracy. Compared to classical differential evolutionary (CDE) algorithm, IDE algorithm is more effective for improving the parameter estimation performance of the ARMA model. The effectiveness of the proposed method is verified by the results of the study.