Abstract:

Food security is a fundamental priority for the nation, the cornerstone of national security, and the baseline for economic security. Wheat is a primary grain crop in China, and flour, as its main processed product, has price fluctuations closely linked to the wheat market, serving as a crucial indicator reflecting supply and demand changes in the grain market. Accurately predicting flour prices is of great significance for stabilizing the consumer market and ensuring national food security. This paper is based on the daily flour price data from the Key Agricultural Product Market Information Platform of the Ministry of Agriculture and Rural Affairs from November 2023 to November 2025, systematically constructing and comparing four time series forecasting models: ARIMA, GM(1,1), LSTM, and Transformer. Sequence analysis reveals complex characteristics of price fluctuations such as stationarity and non-linearity, providing a basis for the subsequent selection and construction of differentiated models. Then, based on a weighted fusion strategy using the reciprocal of squared errors, an Ensemble Integration Model is constructed. Empirical results indicate that individual models each have their own advantages in predictive performance: ARIMA and GM(1,1) perform robustly in depicting overall trends, while LSTM and Transformer play a significant role in capturing non-linear fluctuations. The Ensemble Integration Model, which integrates the advantages of each model and compensates for the limitations of single methods, performs excellently in comprehensive evaluations of key metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Its performance is significantly superior to any single model, demonstrating higher prediction accuracy and stability. The multi-model fusion strategy has significant effectiveness and practical value in flour price forecasting and can be applied to price prediction research for grain and its processed product markets.

Key words: flour price prediction, time series models, model construction, ensemble model