| Autor: |
Folgado, Duarte, Barandas, Marília, Antunes, Margarida, Nunes, Maria Lua, Liu, Hui, Hartmann, Yale, Schultz, Tanja, Gamboa, Hugo |
| Přispěvatelé: |
LIBPhys-UNL |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
This article is a result of the project ConnectedHealth (n.◦46858), supported by Competitiveness and Internationalisation Operational Programme, Portugal (POCI) and Lisbon Regional Operational Programme (LISBOA 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Subsequence search and distance measures are crucial tools in time series data mining. This paper presents our Python package entitled TSSEARCH, which provides a comprehensive set of methods for subsequence search and similarity measurement in time series. These methods are user-customizable for more flexibility and efficient integration into real deployment scenarios. TSSEARCH enables fast exploratory time series data analysis and was validated in the context of human activity recognition and indoor localization. publishersversion published |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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