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 proceedingConference contributionpeer-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 languageEnglish
Title of host publication2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages325-330
Number of pages6
ISBN (Electronic)9781538646625
DOIs
StatePublished - 18 Jun 2018
Externally publishedYes
Event17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Spain
Duration: 18 Dec 201720 Dec 2017

Publication series

Name2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017

Conference

Conference17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017
Country/TerritorySpain
CityBilbao
Period18/12/1720/12/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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