摘要
Abstract
In order to address quality risks associated with the uncertainty in the tobacco primary process,a tobacco forming quality data prediction method that combines the autoregressive integrated moving average(ARIMA)model with the long short-term memory(LSTM)model in deep learning is proposed,and the method is used to predict the outlet tempera-ture of heat treatment(HT)and the inlet moisture of leaf filament warming and humidification in the tobacco primary process of a tobacco enterprise.It evaluates the prediction accuracy of each model using metrics such as root mean square er-ror(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).The results show that the com-bined ARIMA-LSTM model provides more accurate predictions compared to the individual ARIMA and LSTM models.When predicting the HT export temperature,the combined model reduces the RMSE,MAE,and MAPE values by at least 60.1％,63.1％,and 63％respectively.When predicting the inlet moisture content of the leaf conditioning process,the combined model reduces the RMSE,MAE,and MAPE values by at least 49.5％,49.4％,and 49.3％respectively.This method has the potential to provide a reasonable reference for tobacco companies to timely formulate or adjust their produc-tion plans.关键词
ARIMA模型/长短期记忆网络/残差/时序预测/卷烟制丝质量Key words
ARIMA model/long short-term memory networks/residuals/time series forecasting/tobacco primary quality分类
信息技术与安全科学
