Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1111/exsy.12111 | Expert Systems |
Keywords | Field | DocType |
validation of simulation models,performance criteria | Information theory,Data mining,Data set,Information Criteria,Computer science,Simulation modeling,Parametric statistics,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 5 | 0266-4720 |
Citations | PageRank | References |
1 | 0.35 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joana Hora | 1 | 1 | 0.35 |
| Pedro Campos | 2 | 10 | 4.94 |