Obtaining high-precision unit output prediction is a prerequisite for steel enterprises to maintain reasonable and stable inventory levels.Due to the complex and ever-changing production environment and the uncertainty of market demand,the unit output shows certain fluctuations.To this end,a unit output prediction method based on differential evolution and seasonal autoregressive integrated moving average(DE-SARIMA)is proposed.To improve prediction accuracy,a DE algorithm integrating the segmented iterative adaptive mutation strategy and DBSCAN selection strategy is designed to optimize the parameters of the SARIMA model.Testing is carried out using actual production data,and the effectiveness of the proposed method is verified.
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