A Demand Forecasting Method for High Value-Added Agri - Food Based on Machine Learning and Time Series Analysis

This paper presents a hybrid method that combines machine learning and time series forecasting model techniques to enhance the precision and reliability of forecasts. After establishing a learning model for high value-added agri-food demand forecasting, we selected the annual demand for a certain high value-added agri-food as the target value. Other economic indicators, namely the National Income Index, population, and residents' consumption level, were chosen as the key influencing factors affecting product demand. We collected actual demand data sequences from Zhejiang Province for the years 2006–2022 as training and test samples. Comparing the forecasting results, it was found that this hybrid algorithm is an appropriate method for forecasting. Compared to other individual models, it demonstrates higher accuracy and lower mean absolute errors in predicting the demand for high value-added agri-food.