Full Model Selection issue in temporal data through evolutionary algorithms: A brief review

Abstract

In this article, a brief literature review of Full Model Selection (FMS) for temporal data is presented. An analysis of FMS approaches which use evolutionary algorithms to exploit and explore the vast search space found in this kind of problem is presented. The primary motivation of this review is to highlight the scarce published works of FMS in temporal databases. Moreover, a taxonomy for the tasks derived of FMS is proposed and chosen to discuss the different revised approaches. Also, the most representative assessment measures for model selection are described. From the literature review, a set of opportunities and challenges research is presented in the temporal FMS area.