Forecasting using belief functions: An application to marketing econometrics

作者：Kanjanatarakul Orakanya^*; Sriboonchitta Songsak; Denoeux Thierry

来源：International Journal of Approximate Reasoning, 2014, 55(5): 1113-1128.

DOI：10.1016/j.ijar.2014.01.005

• Prediction
• Dempster-Shafer theory
• Evidence theory
• Bass model
• Innovation diffusion
• Sales forecasting

A method is proposed to quantify uncertainty on statistical forecasts using the formalism of belief functions. The approach is based on two steps. In the estimation step, a belief function on the parameter space is constructed from the normalized likelihood given the observed data. In the prediction step, the variable Y to be forecasted is written as a function of the parameter theta and an auxiliary random variable Z with known distribution not depending on the parameter, a model initially proposed by Dempster for statistical inference. Propagating beliefs about theta and Z through this model yields a predictive belief function on Y. The method is demonstrated on the problem of forecasting innovation diffusion using the Bass model, yielding a belief function on the number of adopters of an innovation in some future time period, based on past adoption data.

• 出版日期2014-7