## 2 Background
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Publication** & **Year** & **Domain** & **Ensemble Algorithms** & **Features** \\ \hline
[20] & 2016 & Crude oil price forecasting & EEMD and EELM & \\
[17] & 2016 & Tourism demand forecasting & Bagging, boosting, randomization, and stacking & Daily sale, special days, and promotion days \\
[8] & 2017 & Retail demand forecasting & Boosting & Daily sale, special days, and promotion days \\
[28] & 2017 & Supply chain demand forecasting & Averaging ensemble model & \\
[24] & 2017 & commodities forecasting & Bagging & \\
[36] & 2017 & Hog price forecasting & EEMD & \\
[25] & 2018 & Metro passenger flow forecasting & Averaging ensemble model & Eight neighboring origin–destination (OD) flows are utilized as features for a single target OD flow \\
[29] & 2018 & Food price volatility forecasting & EEMD & \\
[30] & 2019 & Retail demand forecasting & Bagging (RFR) and boosting (GBR and XGBR) & Daily sales \\
[21] & 2019 & Electricity load time series forecasting &. & \\
[37] & 2020 & Energy load forecasting & ETB & Hour, DayOfWeek, IsWorking, Dewpnt, Drybub, prior 1 h, prior 1 day, prior 1 week, and season \\
[31] & 2020 & Wholesale distribution demand forecasting & Weighted and non-weighted, depending on product & Monthly sales, product type, local weather, price promotions, marketing campaigns, holidays, and special events \\
[32] & 2020 & Retail purchase probability forecasting & RF, CNN, XGBoost, and voting classifier & Transactional data and newly generated features \\
[22] & 2022 & Electricity price forecasting & CatBoost & Hourly electricity price, hour of the day, weekend (the current day is weekend or not), and the day name \\
[26] & 2020 & Agribusiness prediction & Bagging (RFR), boosting (GBR and XGBR), and stacking (STACK) & Independent variables (year, month, date, day, weather conditions, public holidays, and festive season). Dependent variables (chicken niryani, mutton biryani, dal takda, pancer lababdar, and card rice) \\
[34] & 2021 & Retail demand forecasting & Extra tree regression & Date, weekly sales, holiday, temperature, fuel price, CPI, and unemployment \\
[33] & 2022 & Retail demand forecasting & Stacking & State, weekly sales, price \\
[27] & 2022 & Steel demand forecasting & Bagging (RFR), boosting (GBR and XGBR), and stacking (STACK) & Availability, raw materials, workers, working days, holidays, down time, and demand level \\
[18] & 2022 & Retail demand forecasting & Majority voting & Fifty-two features related to stores, customers, products, sales, orders, shipping, and delivery \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of selected previous works. These models, showcased through studies ranging from predicting oil prices and electricity load to optimizing passenger flow and commodity prices, consistently demonstrate superior accuracy and performance compared to traditional methods. This trend extends to retail, where ensemble approaches have led to reduced stock outs, increased revenue, and enhanced sales forecasting precision. The remarkable versatility and success of these models across various sectors highlight their potential to reshape and refine demand forecasting practices, ultimately leading to more informed and effective decision-making processes.