A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree

Roberto Rodriguez Urquiaga; Ana Maria Cuadros Valdivia; Reynaldo Alfonte Zapana

doi:10.1109/ISSPIT.2017.8388663

A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree

Roberto Rodriguez Urquiaga, Ana Maria Cuadros Valdivia, Reynaldo Alfonte Zapana

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

Abstract

High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.

Original language	English
Title of host publication	2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	325-330
Number of pages	6
ISBN (Electronic)	9781538646625
DOIs	https://doi.org/10.1109/ISSPIT.2017.8388663
State	Published - 18 Jun 2018
Externally published	Yes
Event	17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Spain Duration: 18 Dec 2017 → 20 Dec 2017

Publication series

Name	2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017

Conference

Conference	17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Country/Territory	Spain
City	Bilbao
Period	18/12/17 → 20/12/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Access to Document

10.1109/ISSPIT.2017.8388663

Cite this

Urquiaga, R. R., Valdivia, A. M. C., & Zapana, R. A. (2018). A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. In 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 (pp. 325-330). (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISSPIT.2017.8388663

Urquiaga, Roberto Rodriguez ; Valdivia, Ana Maria Cuadros ; Zapana, Reynaldo Alfonte. / A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 325-330 (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017).

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abstract = "High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.",

author = "Urquiaga, {Roberto Rodriguez} and Valdivia, {Ana Maria Cuadros} and Zapana, {Reynaldo Alfonte}",

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doi = "10.1109/ISSPIT.2017.8388663",

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Urquiaga, RR, Valdivia, AMC & Zapana, RA 2018, A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. in 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, Institute of Electrical and Electronics Engineers Inc., pp. 325-330, 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, Bilbao, Spain, 18/12/17. https://doi.org/10.1109/ISSPIT.2017.8388663

A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. / Urquiaga, Roberto Rodriguez; Valdivia, Ana Maria Cuadros; Zapana, Reynaldo Alfonte.

2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 325-330 (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017).

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

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PY - 2018/6/18

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DO - 10.1109/ISSPIT.2017.8388663

M3 - Contribución a la conferencia

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PB - Institute of Electrical and Electronics Engineers Inc.

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Urquiaga RR, Valdivia AMC, Zapana RA. A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree. In 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 325-330. (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017). doi: 10.1109/ISSPIT.2017.8388663

A visual analytics approach for exploration of high-dimensional time series based on neighbor-joining tree

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