Article révisé par les pairs
Résumé : In order to make better decisions and take efficient actions in any supply chain system, we need to have better estimation of uncertain parameters, especially the future demands of our customers. To do so we must use a forecasting model which gives the most useful and accurate forecasts. Time series forecasting methods are still one of the most popular approaches used in the business because of their simplicity. One of the most recent methods that caught the attention of researchers and practitioners is Theta method, which was first ranked in M3 competition. This method works based on the decomposition of the deseasonalized original demand data into two components. The first component represents the long-term trend, and the second component indicates the short-term behavior of the data set. ATA method is another method which has been introduced recently. ATA method works like exponential smoothing methods, but in ATA method the smoothing parameter is a function of time point. In this paper we have proposed a new form of Theta method in which we have benefited from the features of ATA and presented a combination of ATA method and Theta method. We have introduced a dynamic model which uses Theta method as the main model and selected from among some alternative methods such as ATA method, simple exponential, and Double Exponential smoothing methods to be used as the theta lines. Also, we optimize the parameters of each method used in the model. Finally, we have tested the mentioned model on a real data set and concluded that the combination of Theta and ATA methods has a better performance compared to the other alternatives in terms of forecast accuracy.