## 1 Introduction  
Exponential smoothing is one of the most widely used techniques in forecasting due to its simplicity, robustness, and accuracy in an automatic forecasting procedure. Hence, it has been widely used for forecasting future values from time series data [1; 2; 3; 4; 5]. One of the basic ideas behind using weighted averages of past observations is that more recent observations carry more weight when determining forecasts than observations in the distant past. Exponential smoothing is divided into three types: single, double, and triple. Single exponential smoothing (SES) is used on data with a stable fluctuating pattern; double exponential smoothing is used on data with a trend pattern; and triple exponential smoothing is used on data with both trend and seasonal patterns [6]. However, there are two problems with exponential smoothing forecasting methods. The first is choosing a suitable value for the smoothing constant (\(\alpha\)) and the second is setting a suitable initial value. The forecaster must determine one or more parameters in exponential smoothing for assigning exponentially decreasing weights as the observations become older, because future events usually depend more on the most recent data [7]. Hence, the value of \(\alpha\) is important for successful forecasting via exponential smoothing. Nevertheless, there need to be consistent guidelines on how they should be selected. In general, the value of \(\alpha\) is selected by applying a suitable calculation process, many of which have been tried out in the past. Many statisticians have recommended that the value of \(\alpha\) should be kept small (in the 0.1 to 0.3 range) to minimize the forecasting error function [8; 9]. Moreover, [PERSON] [10] recommended selecting the value for \(\alpha\) using a nonlinear optimizer [11; 12]. However, it is often the case that the value is outside the recommended range. Solver in Microsoft Excel has become increasingly popular as a nonlinear optimizer [13; 14]. This optimization can be performed using the Solver in Microsoft Excel, and many textbooks, such as [PERSON] and [PERSON] [15] and [PERSON] et al. (2013) [16], mentioned and illustrate this approach. As is well known, SES models are recursive, and thus, an initial value is required to feed the model to obtain the most accurate prediction [6; 17]. Therefore, the choice of this value has an important impact on the forecasting performance. Many researchers have proposed methods to determine a suitable initial value for the smoothing parameter in SES models. [PERSON]'s [18] original suggestion was simply to use the mean of the data for the initial value while others have suggested using the first observation or the average of the first three observations as the initial value. [PERSON] and [PERSON] [17] recommended backcasting to obtain the initial value. In this method, the smoothing algorithm estimated the initial value by going backward in the series. Another approach with a limited number of data points is to use the Bayesian method to combine the prior estimate of the level with the average of the available data [19; 20; 21]. In most cases, the first actual value is considered to be the initial value for the smoothing parameter when using SES [22]. Furthermore, the average of the first five or six observations can be used as the initial value [23], which is used in many statistical packages, such as Minitab, and has been used to set the initial value for SES in many forecasting approaches [24; 25; 26; 27]. The accuracy of SES may vary depending on the chosen value of \(\alpha\). Even though much research has been conducted on this subject, forecasters have not been able to reach a consensus on how to select the value for \(\alpha\) or the initial value. Therefore, in this study, the effects of \(\alpha\) and the initial value are clarified, after which, various methods for setting the initial value are investigated.