## 2 Related Works
### Studies about Demand Forecasting Models  
As stated by [PERSON] et al. (2009), as one of the nonlinear optimization problems in their studies, the forecasting process for oil production was carried out with genetic algorithm and artificial neural network-based models. The authors argued that choosing from input variables would increase the success of the prediction model. This study is important in terms of revealing how important the selection of variables is for the estimation process [8]. [PERSON] (2009) proposed a model for demand forecasting in internet \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Method Description** & **Formula** \\ \hline MSE & Mean squared error & \(mean(e_{t}^{2})\) \\ \hline RMSE & Root Mean squared error & \(\sqrt{MSE}\) \\ \hline MAE & Mean Absolute error & \(mean(|e_{t}|)\) \\ \hline MdaE & Median absolute error & \(median(|e_{t}|)\) \\ \hline MAPE & Mean absolute percentage error & \(mean(|p_{t}|)\) \\ \hline MdAPE & Median absolute percentage error & \(median(|p_{t}|)\) \\ \hline sMAPE & Symmetric mean absolute percentage error & \(mean\Big{(}\frac{2|Y_{t}-F_{t}|}{Y_{t}+F_{t}}\Big{)}\) \\ \hline sMdAPE & Symmetric Median Absolute percentage error & \(median\Big{(}\frac{2|Y_{t}-F_{t}|}{Y_{t}+F_{t}}\Big{)}\) \\ \hline MRAE & Mean relative absolute error & \(mean(|r_{t}|)\) \\ \hline MdRAE & Median relative absolute error & \(median(|r_{t}|)\) \\ \hline GMRAE & Geometric mean relative absolute error & \(mean(|r_{t}|)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demand forecast performance indicators [7]. markets. According to this suggestion, taking advantage of product features to increase the chances of success in demand forecasting can minimize the error level. The proposed model took into account variables such as the status of the product on the showcase, price, lead time, number of products running together, and order frequency. The study, which used 14-month data as a learning process, estimated the next 2 months with MAPE values with 20% and 10.8% absolute errors, respectively, in the model established by the genetic algorithm [9]. [PERSON] et al. (2009) developed a hybrid genetic algorithm-based forecasting model. With this model, which they call the BAFI structure, MSE is the fit function, and the mean squared error is an objective function. They created a forecasting model that would work differently in different situations by using the cause-effect diagram. To investigate the performance of the proposed model, they evaluated the forecasting success according to individual statistical methods for different trends and seasonality conditions. The model they proposed provided the lowest error rate, at around 20% in all conditions. As a result of their studies, the authors emphasized that more models should be used, and at the same time, more successful results can be obtained by changing the genetic algorithm tuning parameters and initial conditions [10]. In a study that argued that traditional methods are not effective in estimating financial time data, [PERSON] (2011) modeled the raw input data financially with wavelet analysis in the first stage and transformed it into a time-scale surface area for forecasting and then divided this surface into various areas with the spectral clustering algorithm. In the second stage, as a result of these distinctions, predictions were made using artificial neural network support vector sets and traditional models. He used the RMSE (root mean square error) indicator while investigating the forecast performances. As a result of his studies, he concluded that the prediction success of the time-scale hybrid model that he proposed is higher than that of traditional methods [11]. In another study, [PERSON] et al. (2011) also took into account some general variables to improve daily consumption forecasts in the retail sector. In the variables, they dealt with weekends or weekdays; holiday days; low, medium, and high price levels; brand categories; and seasons.