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Bicycle Sales Prediction Based on Ensemble Learning
- 1 Department of Industrial Engineering, Capital University of Economics and Business, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
In the field of sales forecasting, there are still various challenges in conducting comprehensive analysis and accurate predictions for bicycle sales, including the diversity of sample data, the range of research scope, and the methods employed. This study aims to fill this research gap by applying a bicycle sales dataset and two ensemble learning methods to investigate the factors influencing bicycle sales and conduct sales predictions and analysis. The research findings indicate that cost, profit, and income are the most significant factors influencing bicycle profit predictions. Compared to the Random Forest model, the Gradient Boosting model performs better in predicting bicycle profits. This paper discusses the relevance and predictive performance of the bicycle sales dataset, providing opportunities for improvement and further optimization in future research to enhance the accuracy and reliability of bicycle sales predictions and offer valuable insights for decision-making and planning. Overall, these results shed light on guiding further exploration of sales prediction.
Keywords
sales forecasting, ensemble learning, the Random Forest model, the Gradient Boosting model
[1]. Christoph D.,and Arnd H. (2017) Case Article—Canyon Bicycles: Judgmental Demand Forecasting in Direct Sales. INFORMS Transactions on Education 17(2):58-62.
[2]. Li, D., Li, X., Gu, F., Pan, Z., Chen, D., and Madden, A. (2023). A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization. Systems, 11(6), 311.
[3]. Silveira Netto, C.F., Bahrami, M., Brei, V.A., Bozkaya, B., Balcisoy, S., and Pentland, A.P. (2023). Disaggregating Sales Prediction: A Gravitational Approach. Expert Systems with Applications, 217, 119565.
[4]. Lyu, X., Jiang, C., Ding, Y., Wang, Z., and Liu, Y. (2019). Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions. Sustainability, 11(3), 913.
[5]. Stephens, T. (2017). A Model for Sales Forecasting and Stock Management. In Business Analytics: Progress on Applications in Asia Pacific (pp. 565-588).
[6]. Kolkova, A., and Rozehnal, P. (2022). Hybrid demand forecasting models: pre-pandemic and pandemic use studies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(3), 699-725.
[7]. Hall, J. (2020). Forecasting off-street bicycle facility demands (Doctoral dissertation).
[8]. Norang, A., Eghbali, M.A., and Hajian, A. (2010, January). Supply chain analysis model based on system dynamics approach: a case of Iranian bicycle manufacturer. In 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM) (Vol. 3, pp. 1481-1485). IEEE.
[9]. Whitlark, D.B., Geurts, M.D., and Swenson, M.J. (1993). New product forecasting with a purchase intention survey. The Journal of Business Forecasting, 12(3), 18.
[10]. Biau, G., Cadre, B., and Rouvière, L. (2019). Accelerated gradient boosting. Machine Learning, 108(6), 971-992.
[11]. Sun, Y. (2022). Research on electricity revenue prediction based on the RF-XGBoost model (Master's thesis, Yanbian University).
Cite this article
Yu,B. (2024). Bicycle Sales Prediction Based on Ensemble Learning. Advances in Economics, Management and Political Sciences,59,293-299.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
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