By modelling the outputs produced by real world systems, we can study and, therefore, understand how they work and behave under different circumstances. This is especially interesting to support the prediction of future behaviour and, consequently, decision-making, what is particularly required in certain application domains. In order to proceed with such modelling, we organise system outputs as time series and study how those series were generated. The study of the time series generation process typically requires specialists and also detailed information on how and where data was obtained from. However, none of them may be available in certain circumstances. Such limitations motivated this paper which presents a survey of techniques commonly used to evaluate and classify time series generation processes and, most importantly, a novel automatic and systematic approach to conduct such task with a minimum of human intervention and subjectivity. By using such approach, researchers can select adequate techniques to model time series, reducing the modelling time and improving the chances to obtain higher accuracy.
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