## 2 Background
The aim of their study was to compare the performance of ensembles (bagging, boosting, and stacking) with reference models such as support vector machine (SVR), multilayer perceptron (MLP), and K-nearest neighbor (KNN) in forecasting prices one month ahead. Their study used monthly time series data for the price of soybean and wheat in the state of Parana, Brazil [26]. In the steel industry, [PERSON] et al. (2022) compared the performance of different machine learning models for demand forecasting in the steel industry. Their study found that the best results came from a combination of models called STACK1 (extreme learning machine + gradient boosting + XGB-SVR [27]. In the retail industry, a new heuristic approach was applied in a Turkish retail chain (SOK Market) with 4000 stores and 1500 SKUs. The results led to a reduction in stock outs, increased revenue by 30%, a 10% decrease in stock days, and a 34% reduction in waste for perishable products [8]. [PERSON] et al. (2017) introduced a new ensemble technique using an averaging method that prioritizes algorithms with good accuracy and reduces deviation from actual sales. The method gives importance to algorithms that perform well based on historical data and penalizes those that deviate from actual sales [28]. [PERSON] et al. (2018) applied ensemble empirical mode decomposition (EEMD) for global food price volatility and decomposed the original food price series into intrinsic mode functions and a residual. In their study, they mentioned that the low-frequency component contributes more to food price volatility, which is caused by notable events and policies. High-frequency components are mainly influenced by small events and market adjustments in a time series analysis. In the long term, food prices are determined by an intrinsic trend from global economic development. The findings reveal that food price volatility is a complex issue with multiple factors affecting both low and high frequencies [29]. In another study, daily optimal ordering quantities of fresh products using six methodologies (LSTM, SVR, RFR, GBR, XGBoost, and ARIMA) were analyzed. The paper compared the performance of conventional statistics, like ARIMA, and various machine learning algorithms, including RNNs (LSTM), SVRs, decision trees/ensemble methods (RFRs), and boosted trees (GBR, XGBoost). It was found that the LSTM and SVR machine learning algorithms outperformed the other demand forecasting models for the dataset [30]. [PERSON] et al. (2020) focused on forecasting sales demand using historical data from a wholesale alcoholic beverage distributor. They employed an ensemble approach by combining traditional statistical models, multivariate models, and deep learning models. The study showed a reduction in the sale forecasting error by almost 50% and 33.5% for the most sold and highest revenue-grossing products respectively, compared to a naive model. The authors concluded that each product needs a unique model for accurate demand forecasting [31]. [PERSON] and [PERSON] (2020) used historical retail purchase data to predict the probability of item purchases. An ensemble learning model was built using random forests (RFs), Convolutional Neural Networks (CNNs), Extreme Gradient Boosting (XGBoost), and a voting mechanism. The model was evaluated using metrics such as accuracy, precision, F1 score, sensitivity, and specificity, and they experienced better performance using ensemble models than existing solutions [32]. [PERSON] et al. (2022) aimed to forecast weekly retail sales using Walmart's retail data from over five years. The forecast subject was divided into twenty-one time series based on different departments and states. Four machine learning models (naive, moving average, prophet, ETS) were used to train the data, and stacking was used as the ensemble technique. The results showed that while the ensemble model using linear regression performed the best in the validation stage, the weighted average method supported by random forests was the best in the testing stage.