Visual Features for Multivariate Time Series

Bao Dien Quoc Nguyen, Rattikorn Hewett, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Visual analytics combines the capabilities of computers and humans to explore the insight of data. It provides coupling interactive visual representations with underlying analytical processes (e.g., visual feature extraction) so that users can utilize their cognitive and reasoning capabilities to perform complex tasks effectively or to make decisions. This paper applies successfulness of visual analytics to multivariate temporal data by proposing an interactive web prototype and an approach that enables users to explore data and detect visual features of interest. A list of nonparametric quantities is proposed to extract visual patterns of time series as well as to compute the similarity between them. The prototype integrates visualization and dimensional reduction techniques to support the exploration processes. Many different temporal datasets are used to justify the effectiveness of this approach, and some remarkable results are presented to show its value.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Advances in Information Technology, IAIT 2020
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450377591
DOIs
StatePublished - Jul 1 2020
Event11th International Conference on Advances in Information Technology, IAIT 2020 - Bangkok, Thailand
Duration: Jul 1 2020Jul 3 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference11th International Conference on Advances in Information Technology, IAIT 2020
CountryThailand
CityBangkok
Period07/1/2007/3/20

Keywords

  • clustering method
  • dimension reduction
  • visual features extraction

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