2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 10 , Issue 2 , PP: 39-51, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Irina V. Pustokhina 1 * , Denis A. Pustokhin 2
Doi: https://doi.org/10.54216/AJBOR.100205
This paper presents a comparative analysis of traditional forecasting methods and machine learning (ML) techniques for sales prediction in e-commerce. We first review the literature on both traditional and ML methods for sales prediction in e-commerce, highlighting their strengths and weaknesses. The study uses a dataset of daily sales from an e-commerce retailer to conduct a comprehensive empirical study thar compares the performance of literature methods from both categories. The analysis considers different forecasting horizons and evaluates the accuracy of the predictions using various performance metrics, such as mean absolute error and mean squared error. The study finds that ML techniques generally outperform traditional methods, especially for longer forecasting horizons. However, some traditional methods, such as the Holt-Winters method, can also perform well under certain conditions. Our study provides insights into the relative strengths and weaknesses of traditional and ML methods for sales prediction in e-commerce and can guide practitioners in selecting appropriate methods for their specific requirements.
Forecasting , Sales Prediction , E-Commerce , machine Learning
[1] Kraus, M., Feuerriegel, S. and Oztekin, A., 2020. Deep learning in business analytics and operations research: Models, applications and managerial implications. European Journal of Operational Research, 281(3), pp.628-641.
[2] Barboza, F., Kimura, H. and Altman, E., 2017. Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, pp.405-417.
[3] Pavlyshenko, B.M., 2019. Machine-learning models for sales time series forecasting. Data, 4(1), p.15.
[4] Bohanec, M., Borštnar, M.K. and Robnik-Šikonja, M., 2017. Explaining machine learning models in sales predictions. Expert Systems with Applications, 71, pp.416-428.
[5] Syam, N. and Sharma, A., 2018. Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, pp.135-146.
[6] Fabbri, M. and Moro, G., 2018, July. Dow Jones Trading with Deep Learning: The Unreasonable Effectiveness of Recurrent Neural Networks. In Data (pp. 142-153).
[7] Vijh, M., Chandola, D., Tikkiwal, V.A. and Kumar, A., 2020. Stock closing price prediction using machine learning techniques. Procedia computer science, 167, pp.599-606.
[8] Park, B. and Bae, J.K., 2015. Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications, 42(6), pp.2928-2934.
[9] Mai, F., Tian, S., Lee, C. and Ma, L., 2019. Deep learning models for bankruptcy prediction using textual disclosures. European journal of operational research, 274(2), pp.743-758.
[10] Chou, J.S. and Nguyen, T.K., 2018. Forward forecast of stock price using sliding-window metaheuristic-optimized machine-learning regression. IEEE Transactions on Industrial Informatics, 14(7), pp.3132-3142.
[11] Dhote, S., Vichoray, C., Pais, R., Baskar, S. and Mohamed Shakeel, P., 2020. Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce. Electronic Commerce Research, 20, pp.259-274.
[12] Nithya, B. and Ilango, V., 2017, June. Predictive analytics in health care using machine learning tools and techniques. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 492-499). IEEE.
[13] Oncharoen, P. and Vateekul, P., 2018, August. Deep learning for stock market prediction using event embedding and technical indicators. In 2018 5th international conference on advanced informatics: concept theory and applications (ICAICTA) (pp. 19-24). IEEE.
[14] Martínez, A., Schmuck, C., Pereverzyev Jr, S., Pirker, C. and Haltmeier, M., 2020. A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, 281(3), pp.588-596.
[15] Marr, B., 2019. Artificial intelligence in practice: how 50 successful companies used AI and machine learning to solve problems. John Wiley & Sons.
[16] Robnik-Šikonja, M. and Bohanec, M., 2018. Perturbation-based explanations of prediction models. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, pp.159-175.
[17] Lee, I. and Shin, Y.J., 2020. Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), pp.157-170.
[18] Henrique, B.M., Sobreiro, V.A. and Kimura, H., 2019. Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, pp.226-251.
[19] Zhu, Y., Zhou, L., Xie, C., Wang, G.J. and Nguyen, T.V., 2019. Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211, pp.22-33.
[20] Cui, R., Gallino, S., Moreno, A. and Zhang, D.J., 2018. The operational value of social media information. Production and Operations Management, 27(10), pp.1749-1769.
[21] Yan, J., Zhang, C., Zha, H., Gong, M., Sun, C., Huang, J., Chu, S. and Yang, X., 2015, February. On machine learning towards predictive sales pipeline analytics. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 29, No. 1).
[22] Tkáč, M. and Verner, R., 2016. Artificial neural networks in business: Two decades of research. Applied Soft Computing, 38, pp.788-804.
[23] McNally, S., Roche, J. and Caton, S., 2018, March. Predicting the price of bitcoin using machine learning. In 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP) (pp. 339-343). IEEE.
[24] Akerkar, R., 2019. Artificial intelligence for business. Springer.
[25] Akinosho, T.D., Oyedele, L.O., Bilal, M., Ajayi, A.O., Delgado, M.D., Akinade, O.O. and Ahmed, A.A., 2020. Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, p.101827.
[26] Bao, Y., Ke, B., Li, B., Yu, Y.J. and Zhang, J., 2020. Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), pp.199-235.
[27] Sharma, A., Bhuriya, D. and Singh, U., 2017, April. Survey of stock market prediction using machine learning approach. In 2017 International conference of electronics, communication and aerospace technology (ICECA) (Vol. 2, pp. 506-509). IEEE.
[28] Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S. and Mosavi, A., 2020. Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, pp.150199-150212.
[29] Fan, C., Zhang, Y., Pan, Y., Li, X., Zhang, C., Yuan, R., Wu, D., Wang, W., Pei, J. and Huang, H., 2019, July. Multi-horizon time series forecasting with temporal attention learning. In Proceedings of the 25th ACM SIGKDD International conference on knowledge discovery & data mining (pp. 2527-2535).
[30] Rafiei, M.H. and Adeli, H., 2016. A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2), p.04015066.
