Vegetable demand forecasting model based on ARIMA

Abstract

Establishing an accurate vegetable demand forecasting model is crucial for fresh supermarkets to optimize replenishment and maximize profits amidst fluctuating market demand driven by the perishable nature of vegetables and changing consumer purchasing decisions. This study takes a certain fresh supermarket as an example, targeting the strong periodicity and seasonality of the vegetable market demand characteristics, using SPSS for data analysis and ARIMA time series modeling. Through stationary testing and white noise testing, the effectiveness and applicability of the model were verified, and an accurate forecast of the total demand for vegetables in the supermarket for the next week was made. This forecasting model provides a reliable basis for the supermarket to formulate replenishment plans, helping it maximize profits in vegetable supply, cope with market demand fluctuations, ensure timely vegetable supply, and meet the constantly changing purchasing needs of consumers.