A total sales forecasting method for a new short life-cycle product in the pre-market period based on an improved evidence theory: application to the film industry

ABSTRACT

It is challenging to forecast total sales of short life-cycle products due to a lack of historical sales data. Multi-source information combination methods make it possible to depict different kinds of characteristics and features, given a limited volume of samples. Evidence theory is a common approach used for multi-source combinations. This paper proposes a new method, named ‘Multi-Evidence Dynamic Weighted Combination Forecasting (MEDWCF)’, based on improvements in the application of Evidence theory. Two kinds of machine learning methods are used to solve the basic probability assignment generation problem pertaining to Evidence theory, so a dynamic update combination algorithm is proposed. These innovations improve the classical one-step static combination rules. Samples of 313 films launched within 2016 and 2017 proved that compared with other forecasting methods, MEDWCF has more effectiveness and better generalisation ability. Effective product sales forecast by MEDWCF may help managers make correct decisions in manufacturing and marketing before the product launched.

KEYWORDS:

• Sales forecasting
• evidence theory
• machine learning
• dynamic update combination algorithm
• box office forecasts

Acknowledgements

This work was supported by the National Nature Science Foundation of China under Grant Number 71672004.

Disclosure statement

No potential conflict of interest was reported by the authors..

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71672004].

Notes on contributors

Zhongjun Tang

Zhongjun Tang is a professor at Beijing University of Technology, China. His primary research interests revolve around demand mining and forecasting, but he also has research interests in production and quality management.

Shunpeng Dong

Shunpeng Dong is a master degree candidate at Beijing University of Technology, China. His primary research interests revolve around demand mining and forecasting.