Classification of growth-models based on growth-rates and its applications

Abstract

In this paper, growth models are classified and characterised using two types of growth rates: from time t to t + 1 and from time t to 2t. They are interesting in themselves but can also be used for a quick prediction of the type of growth model that is valid in a particular case. These ideas are applied on 20 data sets collected by Wolfram, Chu and Lu. We determine (using the above classification as well as via nonlinear regression techniques) that the power model (with exponent > 1) is the best growth model for Sci-Tech online databases, but that Gompertz-S-shaped distribution is the best for social sciences and humanities online databases.