## 3 Materials and Methods
### Methods
#### 3.2.3 Selection of Forecasting Methods  
In demand forecasting, one of the decision variables examined in the literature is the forecast horizon to be used. In some studies, the horizon was chosen on a daily, weekly, or monthly basis. [PERSON] et al. (2011), in their study, took into account the forecasts daily [12]. However, they evaluated the problem since there is a physical market and products with high daily consumption. It is seen as extremely normal that they tend to the forecast horizon daily. When we look at the data of the e-commerce site with the selected dataset in theproposed model, it is seen that the number of the same products that receive orders every day (the rate of sales days is close to 100%) is very low. For this reason, it was thought that it would be logical to only focus on the weekly or monthly forecast horizon alternatives in the decision problem. As a result, a weekly time horizon was chosen in the proposed model. Following the horizon selection, the final issue is deciding which estimation methods will be used for the combination. The studies in the literature were examined to select the estimation methods to be used. Information about some studies and the methods they used are shown in Table 2.  
As can be seen in Table 2, there are many examples of different methods. There are examples of hybrid studies in the e-commerce sector. In our proposed model, which aims to improve the demand forecast performance upon hybridization of the methods, both traditional estimation methods were chosen, and the selected methods were combined with cluster analysis and genetic algorithms. The estimation methods and calculation equations used in this study are as follows:  
Simple Mean Model (SM): The simple arithmetic mean of the observation values of the previous \(t\) horizon can be taken as the predictive value of the next horizon, the horizon (\(t+1\)). \[F_{t+1}=(\frac{\sum_{t=1}^{T}D_{t}}{T}) \tag{1}\]  
Moving Average Model (MA): This method proposed by [44; 45] to eliminate randomness in the time series and takes into account the last k-horizon averages. If the average is to be used as k-horizon averages, this model is preferred. The purpose of this model is to always ensure that a certain number of last horizons have an impact on the forecast. In the study, the simple moving average model was preferred, and two different alternative 2-horizon and 3-horizon models were used for this model. \[F_{t+1}=(\frac{\sum_{t=1}^{k}D_{t-k}}{k}) \tag{2}\]  
Simple Exponential Smoothing (ES): [PERSON] (1963) transformed a stationary time series into a fixed model based on [PERSON]'s formula [46]. From this point of view, he proposed the simple exponential smoothing method. Prediction value: \(F_{t+1}\) is described and used. An important factor in this model is the \(\alpha\) coefficient. While this value is selected between 0 and 1, this value is used to decrease the effect coefficient of the past horizons exponentially. \[F_{t+1}=\alpha Y_{t}+(1-\alpha)F_{t} \tag{3}\]  
[PERSON]'s Two-Parameter Linear Exponential Smoothing Model (HOLT): It is one of the best methods among time series models. [PERSON]'s Two-Parameter Linear Exponential Smoothing Method uses two smoothing constants and three equations to make the estimation. These equations are  
\[F_{t+1}=\alpha Y_{t}+(1-\alpha)(F_{t}+b_{t-1}) \tag{4}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference Study** & **Sector** & **Mode of Operation** & **Used Models** \\ \hline
[10] & FMCG & Hybrid & Statistics and Genetic Algorithm \\ \hline
[38] & Textile & Hybrid & Decision Tree and Cluster (k-means) \\ \hline
[39] & Manufacturing Industry & Hybrid & Holt–Winters and Neural Networks \\ \hline
[40] & Fashion & One Method & Artificial Neural Networks \\ \hline
[41] & Retail & One Method & Meta-Learning Methods \\ \hline
[42] & E-Commerce & Hybrid & LTSM and Particle Swarm Optimization \\ \hline
[43] & Restaurant & One Method & Machine Learning \\ \hline \hline \end{tabular}
\end{table}