Research on Breakfast Supply Prediction in University Canteens Based on LSTM Model
It is an important livelihood project to ensuring the scientific and reasonable supply of breakfast in university canteens.Based on the consumption data of the campus all-in-one card system,the breakfast in the university canteen is statistically classified,and the breakfast supply is studied by using an improved model based on the Long Short Memory Network(LSTM).Five common breakfasts,such as breakfast,fried rice,noodles,congee,and soybean milk,are classified and predicted.The experimental results show that the average root mean square error(RMSE)of the improved LSTM model for predicting five catego-ries is 2.19,and the average absolute error(MAE)is 3.42.Compared with three classic time series mod-els,such as Autoregressive Moving Average(ARAM),Recurrent Neural Network(RNN),and Gated Re-current Unit(GRU),the improved LSTM model performs the best,with high prediction accuracy and reli-ability,providing an effective prediction model for university canteens.
university canteen breakfastlong short-term memorycampus all-in-one card
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