Support Vector Regression for Newspaper/Magazine Sales Forecasting
Xiaodan Yu, Zhiquan Qi, Yuanmeng Zhao · Procedia Computer Science · 2013
Advances in information technologies have changed our lives in many ways. There is a trend that people look for news and stories on the internet. Under this circumstance, it is more urgent for traditional media companies to predict print's (i.e. newspapers/magazines) sales than ever. Previous approaches in newspapers/magazines’ sales forecasting are mainly focused on building regression models based on sample data sets. But such regression models can suffer from the over-fitting problem. Recent theoretical studies in statistics proposed a novel method, namely support vector regression (SVR), to overcome the over-fitting problem. In contrast to traditional regression model, the objective of SVR is to achieve the minimum structural risk rather than the minimum empirical risk. This study, therefore, applied support vector regression to the newspaper/magazines’ sales forecasting problem. The experiment showed that SVR is a superior method in this kind of task.