SSTS: An Intuitive Way To Search In Time Series
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About The Author

Duarte Miguel Folgado has an MSc in Biomedical Engineering and currently works at the R&D department at Associação Fraunhofer Portugal Research. His main research interests include the knowledge extraction from motion sensors, applying computer science, signal processing and machine learning techniques.


SSTS: An Intuitive Way To Search In Time Series

The syntatic tool for pattern Search in Time Series (SSTS) presents a novel approach to query search and pattern matching in time series. The proposed methodology delivers a more interactive and expressive way of matching the desired patterns in time series. Nowadays, data scientists have a plethora of tools at their disposal to manipulate and extract complex information from time series data. However, the available methodologies still require a huge amount of cognitive effort in order to reach desired solutions,...

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