research-article

Visual Features for Multivariate Time Series

Authors:

Bao Dien Quoc Nguyen,

Rattikorn Hewett,

Tommy DangAuthors Info & Claims

IAIT '20: Proceedings of the 11th International Conference on Advances in Information Technology

Article No.: 16, Pages 1 - 8

https://doi.org/10.1145/3406601.3406621

Published: 03 July 2020 Publication History

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.

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Cited By

Evers MWittkowski RLinsen L(2022)ASEVis: Visual Exploration of Active System Ensembles to Define Characteristic Measures2022 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS54862.2022.00039(150-154)Online publication date: Oct-2022
https://doi.org/10.1109/VIS54862.2022.00039
Musleh MChatzimparmpas AJusufi I(2022)Visual analysis of blow molding machine multivariate time series dataJournal of Visualization10.1007/s12650-022-00857-425:6(1329-1342)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s12650-022-00857-4

Index Terms

Visual Features for Multivariate Time Series
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics

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cover image ACM Other conferences

IAIT '20: Proceedings of the 11th International Conference on Advances in Information Technology

July 2020

370 pages

ISBN:9781450377591

DOI:10.1145/3406601

General Chair:
K. Porkaew,
Program Chairs:
M. Chignell,
S. Fong,
B. Watanapa

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Microsoft Corporation: Microsoft Corporation
NECTEC: National Electronics and Computer Technology Center
KMUTT: King Mongkut's University of Technology Thonburi

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2020

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IAIT2020: The 11th International Conference on Advances in Information Technology

July 1 - 3, 2020

Bangkok, Thailand

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Cited By

Evers MWittkowski RLinsen L(2022)ASEVis: Visual Exploration of Active System Ensembles to Define Characteristic Measures2022 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS54862.2022.00039(150-154)Online publication date: Oct-2022
https://doi.org/10.1109/VIS54862.2022.00039
Musleh MChatzimparmpas AJusufi I(2022)Visual analysis of blow molding machine multivariate time series dataJournal of Visualization10.1007/s12650-022-00857-425:6(1329-1342)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s12650-022-00857-4

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