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A review of performance criteria to validate simulation models
Author(s) -
Hora Joana,
Campos Pedro
Publication year - 2015
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12111
Subject(s) - computer science , identification (biology) , parametric statistics , data mining , parametric model , machine learning , statistics , mathematics , botany , biology
This study reviews performance criteria adequate to validate simulation models through the comparison of two quantitative data sets, concerning historical and simulated data. The criteria reviewed were organized according to its characteristics into the groups: error‐based measures, information theory measures, information criteria, parametric tests, non‐parametric tests, distance‐based measures and combined measures. Each criterion is reviewed through its mathematic definition, its applications in literature and the identification of its advantages and drawbacks. The features assessed by each criterion are identified and discussed. This study provides a concise outline over the criteria reviewed, which can be used as a guide to help developers of simulation models into the decision on the most appropriate criteria to validate their models.