In some cases, big data bunches are in the form of Time Series (TS), where the occurrence of complex TS events are rarely presented. In this scenario, learning algorithms need to cope with the TS data balancing problem, which has been barely studied for TS datasets. This research addresses this issue, describing a very simple TS extension of the well-known SMOTE algorithm for balancing datasets. To validate the proposal, it is applied to a realistic dataset publicly available containing epilepsy-related TS. A study on the characteristics of the dataset before and after the performance of this TS balancing algorithm is performed, showing evidence on the requirements for the research on this topic, the energy efficiency of the algorithm and the TS generation process among them.
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