Automatically Forecasting Magazine Demands and Modeling

Finding the optimum quantity of the circulation of magazines is a difficult task due to the effects of promotions, special issues and seasonality. The accuracy of magazine forecasting is very important to prevent oversupply or undersupply because each magazine issue can be thought as a different product due the change of magazines’ cover, contents and promotions from issue to issue. Another important point that oversupplying sales points cause worthless stock or an additional disposal cost after a new magazine issue is released. This paper aims to find a sensible way to model the seasonality and forecast the demand of magazines automatically. For this reason, a youth magazine and a computer magazine from Turkey were studied for about 10.000 sales points respectively. Each sales point has both past sent and past sales data for 58 months from January 2003 to August 2007. Data clarifications are applied due to missing data and unknown actual possible sales for undersupplied sales points. To forecast magazine sales automatically two classical methods and two machine learning methods are compared. Simple Exponential Smoothing and combination of Simple Exponential Smoothing and No Trend Holt-Winters Methods are used as classical methods where multi-layer perceptron and radial basis functio