Research on Market Demand Forecasting Algorithm Based on Machine Learning

In this study, a hybrid algorithm integrating random forest, AdaBoost regression and ARMA model is proposed to improve the accuracy and risk early warning ability of spare parts agents in market demand forecasting. By constructing a large number of decision trees and voting or averaging the results (random forest), the hybrid algorithm effectively deals with the nonlinear relationships and high-dimensional features in the data, and improves the prediction accuracy and stability. By iteratively adjusting the sample weights, each iteration focuses on the samples with the previous misclassification (AdaBoost regression), which enhances the adaptability and robustness of the model to market fluctuations. And by capturing trends and cyclical changes in time series data (ARMA model), it is suitable for time series analysis of demand. Experimental results show that SynthiPred, a hybrid algorithm combining three different types of machine learning methods, achieves significant performance compared with a single model, performs well in terms of accuracy, and effectively supports the risk early warning mechanism. This not only verifies the effectiveness of the proposed algorithm in market demand forecasting, but also demonstrates its application potential in actual business operations, and provides a scientific basis and technical support for the selection of spare parts agents and risk early warning.