## 4 Results and Discussion
### Comparison of ETR and DL  
Demand prediction is of crucial importance in retail--especially when the future demand for perishable products is to be forecasted. In the paper, we have compared tree-based ensembles and LSTM-based deep learning models. The evaluation results for the three product categories are presented in Table 3. Product category A (fruits) comprises around 20 products, product category B (fresh meat) includes around 100 products, and category C (soft drinks) consists of around 200 products. To better understand the performance of the models, we have also included the results for the baseline model, which is the historical moving average, represented as \"MA\" in Table 3. The running times for both models to make forecasts on the test dataset were consistently below one minute, demonstrating that both of our models exhibit efficient computational performance in our use case. Overall, in all three product categories, both the ETR and DL models clearly outperformed the baseline (MA) model according to the evaluation metrics, as performed, e.g., by [PERSON] et al. (2022) to compare LSTM performance in the context of bike sharing demand prediction with baseline models [35]. This highlights the advantage of using machine learning models over traditional methods. The table also shows that in all three product categories, the ETR model performs better than the DL model in terms of all evaluation metrics. The performance difference between these two methods is especially noticeable in product category B (fresh meat). As mentioned, the models were trained on an 80% training set aggregating around 4.1 million \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product Category** & **Model Name** & **MAPE** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{A} & MA & 22.53\% & 2053.06 & 2728.39 & 0.06 \\  & ETR & 12.29\% & 1141.47 & 1794.09 & 0.60 \\  & DL & 12.33\% & 1199.89 & 1840.46 & 0.59 \\  & MA & 35.00\% & 963.48 & 1216.12 & 0.05 \\ \multirow{2}{*}{B} & ETR & 12.48\% & 431.92 & 805.47 & 0.58 \\  & DL & 16.63\% & 569.69 & 922.02 & 0.45 \\ \multirow{2}{*}{C} & MA & 27.08\% & 5299.60 & 7745.05 & 0.01 \\  & ETR & 10.56\% & 2344.92 & 5193.20 & 0.55 \\ \multirow{2}{*}{C} & DL & 12.33\% & 2768.68 & 5549.90 & 0.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model evaluation results. records and tested on a 20% test set, aggregating around 1.1 million records. Their outcomes were compared with real-world data, i.e., the real demand as recorded in the test data. Overall, we have revealed that our results are in line with previous research [17; 18; 19] where the accuracy of ensemble models is higher than traditional models and deep learning models. In addition, our results support the expectation that tree-based models would perform better than other models [10]. Contrary to the findings of [PERSON] et al. (2022), our results show that the ETR model performed better in both the train and test datasets [33]. Similarly, in previous research, it was found that LSTM outperformed traditional machine learning models, like random forests and extra tree regressors (ETRs), in predicting the short-term demand for shared bikes. The results showed significantly better results for LSTM (R2 score = 0.922, RMSE = 314.17) than ETR (R2 score = 0,724, RMSE = 487.95). A focus was on the hourly prediction of bike demand using publicly available data on shared bikes in London [48]. Compared to our current study, where the prediction horizon spans over a year with a focus on daily predictions, LSTM proved to be more powerful on this very short-term level. It would be interesting to compare their results and our results on a long-term basis. In [PERSON] et al. (2021), ETR emerged as the most effective method for forecasting future sales of Walmart stores, closely followed by the random forest regression.