Sales Forecasting for New Products Using Homogeneity-Based Clustering and Ensemble Method

Accurate sales forecasting for new products is critical in industries characterized by intense competition, rapid innovation, and short product life cycles, such as the smartphone market. This study proposes a data-driven framework that enhances prediction accuracy by combining homogeneity-based clustering with an ensemble learning approach. Unlike traditional methods that depend on product-specific attributes, our approach utilizes historical sales data from similar products, overcoming attribute dependency. Using K-means clustering, the training data are segmented into homogeneous groups, and tailored ensemble forecasting models are developed for each cluster by combining five machine learning models: Random Forest, Extra Tree, XGBoost, LightGBM, and TabNet. When tested on South Korean smartphone sales data, the framework achieves superior performance, with the optimal ensemble model using four clusters delivering an MAPE of 8.3309% and an RMSPE of 7.8360%, significantly outperforming traditional single-cluster models. These findings demonstrate the effectiveness of leveraging data homogeneity and ensemble methods, offering a scalable and adaptable solution for accurate sales forecasting of new products.