2024
DOI: 10.1186/s40537-024-00924-7
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Evaluation is key: a survey on evaluation measures for synthetic time series

Michael Stenger,
Robert Leppich,
Ian Foster
et al.

Abstract: Synthetic data generation describes the process of learning the underlying distribution of a given real dataset in a model, which is, in turn, sampled to produce new data objects still adhering to the original distribution. This approach often finds application where circumstances limit the availability or usability of real-world datasets, for instance, in health care due to privacy concerns. While image synthesis has received much attention in the past, time series are key for many practical (e.g., industrial… Show more

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