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Author: Admin | 2025-04-27
These models, in product categories A and B, ETR stands out with a slight advantage in terms of both MAPE and MAE.Conversely, in product category C, the GBR demonstrates a slight advantage across all metrics, including MAPE, MAE, RMSE, and R2. This suggests that the GBR may be better suited for this specific category, where the underlying data patterns may be better captured by its boosting approach.These findings highlight the robustness and consistent performance of ensemble tree models across diverse product categories. However, it is crucial to emphasize that the choice of the most suitable model should be made carefully, considering the unique requirements and objectives of the application. Additionally, the relative importance of different performance metrics should be carefully weighed against each other, as they can provide insights into various aspects of predictive performance. 5. Conclusions, Limitations, and OutlookThe purpose of this paper is to enhance the accuracy of demand forecasting by investigating ensemble demand forecasting approaches and comparing selected techniques using real-life data. We assessed a tree-based ensemble model and a deep learning model on supermarket store data. Our analysis of the ETR models yielded several valuable insights. Firstly, ETR requires less data preparation, such as feature scaling. Secondly, ETR generates its own feature importance metrics, which is highly beneficial for model interpretability. Thirdly, ETR is quicker to train and tune since it has fewer hyperparameters compared to DL. Finally, defining the best network structure can be a complex task in DL, whereas ETR methods do not have such requirements. To strengthen the results, we have compared the ETR results with three additional, acknowledged tree-based ensemble models, i.e., RFR, XGB, and GBR. As expected, the results for ETR and these three additional tree-based ensemble approaches were very similar.Our paper’s contribution can be summarized in the following ways. First, we extend beyond using only historical demand data by incorporating diverse features, such as price and external factors, like weather and COVID-19-related data. This enriched dataset enables a more comprehensive understanding of demand behavior. Second, our study employs a substantial dataset from a prominent supermarket, ensuring the real-world applicability of our findings. This authenticity lends credibility to our research and enhances its practical relevance. Third, we leverage the power of advanced machine learning techniques by employing two state-of-the-art models. These models are carefully chosen for their ability to handle complex datasets and capture intricate demand patterns effectively. Lastly, to comprehensively
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