Predicting Red Wine Sales Using Deep Learning Algorithms for Market Performance Optimisation
DOI:
https://doi.org/10.63561/jmns.v2i4.1127Keywords:
Red wine forecasting, Deep learning, Time series analysis, Sales prediction, Neural networksAbstract
The demand for accurate sales forecasting within the wine industry has increased as producers, distributors, and retailers seek to improve production planning, inventory management, and marketing strategies. This study presents a deep learning-based approach for forecasting red wine sales using time-series data. Traditional statistical methods such as ARIMA and Exponential Smoothing have demonstrated limited performance when applied to volatile, nonlinear datasets typical of wine markets. To overcome these challenges, the proposed model employs recurrent neural networks (RNNs) and other deep learning architectures to capture long-term dependencies and complex relationships among features. The model was developed using Python and evaluated against traditional statistical models using metrics such as Mean Squared Error (MSE). The results demonstrate that deep learning methods outperform classical models in predictive accuracy and robustness. This research provides a foundation for intelligent forecasting systems that can enhance market optimization and support data-driven decision-making in the wine industry.
References
Agatz, N.A., Fleischmann, M., & Nunen, J. V. (2006). E-fulfillment and multi-channel distribution - A review. Eur. J. Oper. Res., 187, 339-356. http://doi.org/10.1016/j.ejor.2007.04.024
Alihodžić, A., Zvorničanin, E., & Čunjalo, F. (2022). A Comparison of Machine Learning Methods for Forecasting Dow Jones Stock Index. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing, Lecture Notes in Computer Science, 13127. Springer, Cham. https://doi.org/10.1007/978-3-030-97549-4_24
Babić Rosario, A., Sotgiu, F.D., de Valck, K., & Bijmolt, T.H. (2016). The Effect of Electronic Word of Mouth on Sales: A Meta-Analytic Review of Platform, Product, and Metric Factors. Journal of Marketing Research, 53, 297 - 318. http://doi.org/10.1509/jmr.14.0380
Bergmeir, C., & Benítez, J. M. (2017). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213. https://doi.org/10..1016/j.ins.2011.12.028
Bower, A.B., & Maxham, J.G. (2012). Return Shipping Policies of Online Retailers: Normative Assumptions and the Long-Term Consequences of Fee and Free Returns. Journal of Marketing, 76, 110 - 124. http://doi.org/10.1509/jm.10.0419
Cerqueira, V., Torgo, L., Smailovic, J., & Mozetic, I. (2017). A Comparative Study of Performance Estimation Methods for Time Series Forecasting. In 2017 IEEE international conference on data science and advanced analytics (DSAA), 529–538. IEEE. https://doi.org/10.1109/DSAA.2017.7
Chen, L., Wu, W., Zhang, B., Wen, Y. & Yang, C. (2021). Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting. http://arxiv.org/abs/2111.04942
Dietterich, M., Jerath, K., & Srinivasan, K. (2017). Firm Strategies in the “Mid Tail” of Platform-Based Retailing. Marketing Science, 30 (5), 757–775. https://doi.org/10.1287/mksc.1110.0656
Duan, Q., Wang, Q., & Song, P. (2022). The Effects of Agency Selling on Reselling on Hybrid Retail Platforms. International Journal of Electronic Commerce, 23 (4), 524–556. https://doi.org/10.1080/10864415.2019.1655209
Elsayed, S., Thyssens, D., Rashed, A., Jomaa, H. S., & Schmidt-Thieme, L. (2021). Do We Really Need Deep Learning Models for Time Series Forecasting? Retrieved from http://arxiv.org/abs/2101.02118
Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an Online Retailer: Demand Forecasting and Price Optimization. Manufacturing & Service Operations Management, 18 (1), 69–88. https://doi.org/10.1287/msom.2015.0561
Hastie, T., Tibshirani, R., & Friedman, J. (2017). Model Assessment and Selection. In The elements of statistical learning, 219–259. https://doi.org/10.1007/978-0-387-84858-7{_}7
Huang, S., & Lin, F. (2005). Designing intelligent sales-agent for online selling. International Conference on Evolutionary Computation, 279 – 286. https://doi.org/10.1145/1089551.1089605
Islam, S.F.N., Sholahuddin, A., & Abdullah, A.S. (2021). Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah .Journal of Physics: Conference Series, 1722 (1), 012016. https://doi.org/10.1088/1742-6596/1722/1/012016
Ito, S., & Fujimaki, R. (2017). Optimization Beyond Prediction. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (Vol. Part F129685, pp. 1833–1841). New York, NY, USA: ACM. Retrieved from https://doi.org/10.1145/3097983.3098188
Januschowski, R., Chocarro, R., Cortiñas, M., Elorz, M., & Mugica, J. (2020). Channel Choice in the 21st Century: The Hidden Role of Distribution Services. Journal of Interactive Marketing, 33, 1–12. https://doi.org/10.1016/j.intmar.2020.09.002
Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259 (2), 689–702. https://doi.org/10.1016/j.ejor.2016.10.031
Livieris, I.E., Pintelas, E.G., & Pintelas, P.E. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural Computing and Applications, 32, 17351 – 17360. https://doi.org/10.1007/s00521-020-04867-x
Makridakis, D., Hosanagar, K., & Nair, H. S. (2022a). Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook. Management Science, 64 (11), 5105–5131. http://pubsonline.informs.org/doi/10.1287/mnsc.2017.2902
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022b). The M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting.
Onu, F. U., Igwe J. S., Ikporo, S. C., Ariom, L. E., Nwamini B. T., & Onwudebelu, U. (2023), Security Vulnerabilities in Some Popular Object-Oriented Programming Languages – Forecast of the Danger Ahead, Afr. J. Comp. & ICT, 16, (1/ 2). https://afrjcict.net
Onwudebelu, U., Ugah, J.O., & Fasola, O.O. (2025). Harnessing Disruptive Technologies for Flood Prediction and Advisory Systems through Persuasive Modeling. Digital Technologies Research and Applications. 4(3), 113-135. https://doi.org/10.54963/dtra.v4i3.1648
Onwudebelu, U.; Ugah J. O., Uket N. E., E.B., Nnabu-Richard N. E., Oketa, K.C, Achi, I.I. & Afolabi I. Y. (2024). FPAS Model: A Case Study of Flood Prediction and Advisory System. Sumerianz Journal of Scientific Research. 2024, 7, 7–19. https://doi.org/10.47752/sjsr.72.7.19
Schwindt, C., Coletto, V., Díaz, M.M. & Ponzoni, I. (2023). Could QSOR Modelling and Machine Learning Techniques Be Useful to Predict Wine Aroma? Food Bioprocess Technol 16(1), 24–42. 2023. https://doi.org/10.1007/s11947-022-02836-x
Tiwari, P., Bhardwaj, P., Somin, S., Parr, W. V., Harrison, R. & Kulasiri, D. (2022). Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way?, 19, Foods 11(19), 3072. https://doi.org/10.3390/foods11193072
Ugah, J.O. & Onwudebelu, U. FPAS System: The New Frontier in Flood Prediction and the Lifesaver for Flood‑Prone Communities. Afr. J. Manag. Inf. Syst. 2023, 5, 15–35. https://afrjmis.net/wp-content/uploads/2024/08/vol5iss123pap2journalformatpn.pdf
Vijayalakshmi, M.N., murli, V., Menezes, D.B., Menon, R., Divecha, A., Ravindran, R., & Mehta, K. (2010). Intelligent sales forecasting engine using genetic algorithms. Proceedings of the 19th ACM international conference on Information and knowledge management.
Zhang, Q., Zeng, Z., Si, K., Luo, Q., Chen, D., & Chen, D. (2023a). Quality Prediction of Brewing Koji based on Multitask Learning, In 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI 2023), 1-7. https://doi.org/10.1145/3653081.3653090
Zhang, Z., Li, Z., Shu, Z., Liang, J., Bai, W., Fei, Y., Zhao, W. & Zhao, S. (2023b). Effects of different koji making methods on volatile flavor components in the production of fermented soybean flavor Baijiu koji, Journal of Food Safety and Quality Testing 14(14), 105–114. http://doi.org/10.19812/j.cnki.jfsq11-5956/ts.2023.14.008
