Pre-release sales forecasting: A model-driven context feature extraction approach

Abstract

Social media can be a potential data source in new product pre-release sales forecasting. However, the relationship is not straightforward because sales volume is the result of the interaction between different context entities. To reveal the important hidden dimensions from the structural relationships between context entities, we propose a model-driven feature extraction approach. Based on the semantic entity relationship model, middle-level features can be automatically generated through predefined primitive features with respect to typical structural relationships. Then, spatio-temporal analytics is applied in feature selection for sales forecasting models. The proposed technical approach is verified in movie box-office forecasting for a leading cinema chain in China.