Forecasting monthly data using total and split exponential smoothing

• Main Authors: Mak, Kit Mun, Choo, Wei Chong, Md Nassir, Annuar
• Format: Article
• Language: English
• Published: Faculty of Economics and Management, Universiti Putra Malaysia 2018
• Online Access: http://psasir.upm.edu.my/id/eprint/22651/1/27%29%20Forecasting%20Monthly%20Data.pdf; http://psasir.upm.edu.my/id/eprint/22651/; http://www.ijem.upm.edu.my/vol12_noS2/27)%20Forecasting%20Monthly%20Data.pdf

In the motion picture industry, the movie market players always rely on accurate demand forecasts. Distributors require the demand forecasts to make decisions such as marketing strategy and costs, number of screens, and release timing. Movie demand is known to show seasonality. Thus, forecasting methods which are able to capture such patterns can be relied on to produce an accurate prediction. In this paper, we study the performance of the recently proposed exponential smoothing method. It is known as total and split exponential smoothing, and applies it to box office from the United States on monthly basis. The forecasts are evaluated against other seasonal exponential smoothing methods. Overall, total and split exponential smoothing with subjectively chosen parameters was performing well, followed by seasonal damped trend exponential smoothing method (DA-M).