TY - GEN
T1 - Visual Features for Multivariate Time Series
AU - Nguyen, Bao Dien Quoc
AU - Hewett, Rattikorn
AU - Dang, Tommy
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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.
AB - 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.
KW - clustering method
KW - dimension reduction
KW - visual features extraction
UR - http://www.scopus.com/inward/record.url?scp=85123042409&partnerID=8YFLogxK
U2 - 10.1145/3406601.3406621
DO - 10.1145/3406601.3406621
M3 - Conference contribution
AN - SCOPUS:85123042409
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 11th International Conference on Advances in Information Technology, IAIT 2020
PB - Association for Computing Machinery
T2 - 11th International Conference on Advances in Information Technology, IAIT 2020
Y2 - 1 July 2020 through 3 July 2020
ER -