Characterization of climatological time series using autoencoders

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

doi:10.1109/LA-CCI.2017.8285717

Characterization of climatological time series using autoencoders

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

Departamento Académico de Ingeniería de Sistemas e Informática

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538637340
DOIs	https://doi.org/10.1109/LA-CCI.2017.8285717
State	Published - 7 Feb 2018
Event	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Arequipa, Peru Duration: 8 Nov 2017 → 10 Nov 2017

Publication series

Name	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Volume	2017-November

Conference

Conference	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
Country/Territory	Peru
City	Arequipa
Period	8/11/17 → 10/11/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

autoencoder
Dimensionality reduction
time series

Access to Document

10.1109/LA-CCI.2017.8285717

Cite this

Zapana, R. A., Lopez Del Alamo, C. J., Quenaya, J. F. L., & Valdivia, A. M. C. (2018). Characterization of climatological time series using autoencoders. In 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings (pp. 1-6). (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LA-CCI.2017.8285717

Zapana, Reynaldo Alfonte ; Lopez Del Alamo, Cristian José ; Quenaya, Jan Franco Llerena et al. / Characterization of climatological time series using autoencoders. 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6 (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings).

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abstract = "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.",

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Zapana, RA, Lopez Del Alamo, CJ, Quenaya, JFL & Valdivia, AMC 2018, Characterization of climatological time series using autoencoders. in 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings, vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017, Arequipa, Peru, 8/11/17. https://doi.org/10.1109/LA-CCI.2017.8285717

Characterization of climatological time series using autoencoders. / Zapana, Reynaldo Alfonte; Lopez Del Alamo, Cristian José; Quenaya, Jan Franco Llerena et al.

2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6 (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings; Vol. 2017-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lopez Del Alamo, Cristian José

AU - Quenaya, Jan Franco Llerena

AU - Valdivia, Ana Maria Cuadros

PY - 2018/2/7

Y1 - 2018/2/7

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AB - 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.

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KW - Dimensionality reduction

KW - time series

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Zapana RA, Lopez Del Alamo CJ, Quenaya JFL, Valdivia AMC. Characterization of climatological time series using autoencoders. In 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6. (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings). doi: 10.1109/LA-CCI.2017.8285717

Characterization of climatological time series using autoencoders

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