Sales Forecasting Using Machine Learning Models: A Comparative Study
DOI:
https://doi.org/10.63561/jca.v3i1.1202Keywords:
Sales Forecasting, Machine Learning, Data Collection, Training, EvaluationAbstract
Sales forecasting is a critical activity for businesses, enabling them to make informed decisions about production, inventory, marketing, and other key areas. Traditional sales forecasting methods, such as time series analysis and statistical regression, are often unable to capture the complex relationships between factors influencing sales, leading to potential inaccuracies. This study aims to address these limitations by developing a sales forecasting model using deep learning and machine learning techniques. Deep learning methods, capable of modeling complex relationships and adapting to changing market conditions, are expected to improve the accuracy and cost-effectiveness of sales forecasting. This study involves data collection and preprocessing, model development and evaluation. LSTM model, performed quite well with an accuracy of approximately 80%. The Random Forest Regressor and XGB Regressor outperformed the LSTM model with accuracy scores of 94.79% and 98.7% respectively. On the other hand, the Linear Regressor model underperformed, delivering an accuracy of just 57.72%. This study reinforces the idea that a combination of traditional forecasting methods, machine learning, and deep learning models can be utilized effectively to gain more accurate sales forecasts. Based on the findings and conclusions of this study, it is recommended that the models should be continuously updated and improved with the influx of new data, to adapt to changing market conditions and trends.
References
Abdulrahman, A., & Baykara, M. (2020). Fake news detection using machine learning and deep learning algorithms. 2020 International Conference on Advanced Science and Engineering (ICOASE), 18–23. https://doi.org/10.1109/ICOASE51841.2020.9436605
Adenusi, C. A., Vincent, O. R., Bolarinwa, J., & Oluwakemi, O. (2022). Predicting the pandemic effect of covid 19 on the nigeria economic, crude oil as a measure parameter using machine learning. In Decision Sciences for COVID-19: Learning Through Case Studies (pp. 79–93). Springer.
Boukrouh, I., Idiri, S., Tayalati, F., Azmani, A., & Bouhsaien, L. (2025). A hybrid approach for sales forecasting: Combining deep learning and time series analysis. International Journal of Engineering, Transactions A: Basics, 38(4), 859–870. https://doi.org/10.5829/ije.2025.38.04a.16
Chandriah, K. K., & Naraganahalli, R. V. (2021). RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-021-10913-0
Cheriyan, S., Ibrahim, S., Mohanan, S., & Treesa, S. (2018). Intelligent sales prediction using machine learning techniques. 53–58.
Chung, D., Lee, C. G., & Yang, S. (2023). A hybrid machine learning model for demand forecasting: Combination of K-means, Elastic-Net, and Gaussian process regression. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 325–336.
Elalem, Y. K., Maier, S., & Seifert, R. W. (2023). A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks. International Journal of Forecasting, 39(4), 1874-1894.
Fathallah, A., Abdi, L., & Douik, A. (2017). Facial expression recognition via deep learning. 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 745–750. https://doi.org/10.1109/AICCSA.2017.124
Feizabadi, J. (2022). Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 25(2), 119–142. https://doi.org/10.1080/13675567.2020.1803246
Green, F. (2022). Machine-learning Sales Forecasting: A Review. Sage Science Review of Applied Machine Learning, 5(1), 1–21.
Huber, J., & Stuckenschmidt, H. (2020). Daily retail demand forecasting using machine learning with emphasis on calendric special days. International Journal of Forecasting. Advance online publication. https://doi.org/10.1016/j.ijforecast.2020.02.005
Kilimci, Z. H., Akyuz, A. O., Uysal, M., Akyokus, S., Uysal, M. O., Bulbul, B. A., & Ekmis, M. A. (2019). An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity, 2019, Article 9067367. https://doi.org/10.1155/2019/9067367
Mediavilla, M. A., Dietrich, F., & Palma, D. (2022). Review and analysis of artificial intelligence methods for demand forecasting in supply chain management. Procedia CIRP, 107, 1126–1131. https://doi.org/10.1016/j.procir.2022.05.119
Punia, S., Nikolopoulos, K., Singh, S. P., Madaan, J. K., & Litsiou, K. (2020). Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1735666
Shilong, Z. (2021). Machine learning model for sales forecasting by using XGBoost. 480–483.
Wusu, G. E., Alaka, H., Yusuf, W., Mporas, I., Toriola-Coker, L., & Oseghale, R. (2022). A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria. Smart and Sustainable Built Environment, ahead-of-print.
Xie, G., Qian, Y., & Wang, S. (2021). Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tourism Management, 82, 104208. https://doi.org/10.1016/j.tourman.2020.104208
