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This paper presents an evaluation framework that attempts to quantify the"degree of realism" of simulated financial time series, whatever the simulationmethod could be, with the aim of discover unknown characteristics that are notbeing properly reproduced by such methods in order to improve them. For thatpurpose, the evaluation framework is posed as a machine learning problem inwhich some given time series examples have to be classified as simulated orreal financial time series. The "challenge" is proposed as an open competition,similar to those published at the Kaggle platform, in which participants mustsend their classification results along with a description of the features andthe classifiers used. The results of these "challenges" have revealed someinteresting properties of financial data, and have lead to substantialimprovements in our simulation methods under research, some of which will bedescribed in this work.

The ETS challenges: a machine learning approach to the evaluation of simulated financial time series for improving generation processes