Research on vehicle spare parts demand forecast based on XGBoost-LightGBM

Abstract

Vehicle spare parts demand forecasting is crucial for optimizing inventory and improving maintenance efficiency. This study aims to explore a vehicle spare parts demand forecasting method based on the fusion of XGBoost and LightGBM models to enhance prediction accuracy and precision. In this paper, we first collected a large amount of historical spare parts demand data and associated feature data, followed by data preprocessing and feature engineering. Then, we constructed individual machine learning models as well as the XGBoost-LightGBM fusion model, and performed parameter tuning and optimization using the Optuna framework. Experimental results demonstrate that both XGBoost and LightGBM models achieve favorable performance in spare parts demand forecasting, but the fusion of these two models further enhances prediction accuracy. The fusion model exhibits lower MAPE values compared to individual models on the test set, confirming its superiority and effectiveness. This method leverages the strengths of both models and improves prediction accuracy through weight fusion, offering practical significance in achieving accurate spare parts demand forecasting, optimizing inventory, and improving maintenance efficiency. Future research can explore alternative machine learning algorithms and feature engineering methods to further enhance the accuracy and reliability of vehicle spare parts forecasting.