This is the final version. Available from Elsevier via the DOI in this record. Data availability:
The data that has been used is confidential.The rapid development of machine learning algorithms provides new solutions for predicting the quantity of
recycled end-of-life products. However, the Stacking ensemble model is less widely used in the field of predicting
the quantity of recycled end-of-life products. To fill this gap, we propose a Stacking ensemble model that utilizes
support vector regression, multi-layer perceptrons, and extreme gradient boosting algorithms as base models,
and linear regression as the meta model. The k-nearest neighbor mega-trend diffusion method is applied to avoid
overfitting problems caused by a small sample data set. The grid search and time series cross validation methods
are utilized to optimize the proposed model. To verify and validate the proposed model, data related to China’s
end-of-life vehicles industry from 2006 to 2020 is used. The experimental results demonstrate that the proposed
model achieves higher prediction accuracy and generalization ability in predicting the quantity of recycled end-of-life product
