Feature selection for Time Series Clustering: A case study on Dengue in Peru

Autores

  • Maria Giohanna Martinez
  • Diego H. Stalder
  • Juan Vicente Bogado
  • Christian E. Schaerer
  • Max Ramírez-Soto M.
  • Denisse Champin

Resumo

In recent decades, the world has experienced a health crisis due to the increase of infectious diseases cases, such as COVID-19, Dengue, Zika, among others. Dengue is one of the world’s most important neglected tropical disease transmitted by vectors, mainly Aedes Aegypti. [...]

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Biografia do Autor

Maria Giohanna Martinez

National University of Asuncion, Paraguay

Diego H. Stalder

National University of Asuncion, Paraguay

Juan Vicente Bogado

National University of Asuncion, Paraguay

Christian E. Schaerer

National University of Asuncion, Paraguay

Max Ramírez-Soto M.

Technological University of Peru, Lima, Peru

Denisse Champin

Technological University of Peru, Lima, Peru

Referências

S. Aghabozorgi, A. S. Shirkhorshidi, and T. Y. Wah. “Time-series clustering – A decade review”. In: Information Systems 53 (2015), pp. 16–38. issn: 0306-4379. doi: 10.1016/j. is.2015.04.007.

J. V. Bogado et al. “Time Series Clustering to Improve Dengue Cases Forecasting with Deep Learning”. In: 2021 XLVII Latin American Computing Conference (CLEI) (2021), pp. 1–10. doi: 10.1109/CLEI53233.2021.9640130.

CDC. Centers for Disease Control and Prevention Official Site. Online. Accessed 10/02/2022, https://www.cdc.gov/.

R. J. Hyndman, E. Wang, and N. Laptev. “Large-Scale Unusual Time Series Detection”. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (2015), pp. 1616–1619. doi: 10.1109/ICDMW.2015.104.

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Publicado

2022-12-08

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