The application of synthetic data generation and data-driven modelling in the development of a fraud detection system for fuel bunkering

Nonparametric Generation of Synthetic Data Using Copulas

“…The generated data and the real data are compared using scatter plots since they are presented only as an illustration. To generate the data x (1) , . .…”

“…To prove by statistical inference that the points (D F n (z j ), D G m (z j )) lie on the straight line joining the points (0, 0) and (1,1), it is enough to show that the tuples follow a linear relationship and that, if a simple linear regression is fitted, then the confidence interval of the intercept β 0 contains zero, the confidence interval of the slope β 1 contains one, and the coefficient of determination R 2 is very close to one. We follow the approach described to show that the generated data come from the same distribution as the original data; for this, we estimate bootstrap confidence intervals of 95% by the percentile method, as explained in ( [37], pp.…”

“…Synthetic data (SD) is data generated through mathematical models that preserve the statistical properties of real data (RD), such as the marginal and joint distributions of the data variables [1,2]. In recent years, research on synthetic data generation processes has become more relevant since important applications have been demonstrated, such as the possibility of anonymizing information, with a special interest in health [3][4][5], balancing classes in the training of machine learning (ML) models [6][7][8][9], increasing the amount of data to improve the generalizability of deep learning models [10][11][12], among others.…”