Characterization of climatological time series using autoencoders

Reynaldo Alfonte Zapana, Cristian José Lopez Del Alamo, Jan Franco Llerena Quenaya, Ana Maria Cuadros Valdivia

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Common problems in climatological time series data are high dimensionality, correlation between the sequential values and noise due to calibration of meteorological stations influencing dramatically in the quality of clustering, classification, climate pattern finding and data processing. One way to deal with this problem is through feature extraction technique. In order to extract features from large climatological time series data, we propose a feature extraction method based on autoencoder neural network (AUTOE). As a first step, time series is standardized. Then, different architectures of autoencoder is applied on it to reduce dimensionality. Finally, k-means clustering algorithm are used to evaluate them through quality measures. As a result, autoencoder performs well and is competitive with other feature extraction techniques over Synthetic Control Chart Time Series.

Idioma originalInglés
Título de la publicación alojada2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1-6
Número de páginas6
ISBN (versión digital)9781538637340
DOI
EstadoPublicada - 7 feb. 2018
Evento2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Arequipa, Perú
Duración: 8 nov. 201710 nov. 2017

Serie de la publicación

Nombre2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Volumen2017-November

Conferencia

Conferencia2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
País/TerritorioPerú
CiudadArequipa
Período8/11/1710/11/17

Nota bibliográfica

Publisher Copyright:
© 2017 IEEE.

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