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Article Dans Une Revue International Journal of Approximate Reasoning Année : 2014

Forecasting using belief functions: An application to marketing econometrics

Résumé

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 θ 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 θ 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.
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Dates et versions

hal-01127783 , version 1 (08-03-2015)

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Orakanya Kanjanatarakul, Songsak Sriboonchitta, Thierry Denoeux. Forecasting using belief functions: An application to marketing econometrics. International Journal of Approximate Reasoning, 2014, 55 (5), pp.1113-1128. ⟨10.1016/j.ijar.2014.01.005⟩. ⟨hal-01127783⟩
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