Abstract

Many applications generate and/or consume multi-variate temporal data, and experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this article, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time series along with external knowledge, including variate relationships that are known a priori. Relying on these observations, we develop data models and algorithms to detect robust multi-variate temporal (RMT) features that can be indexed for efficient and accurate retrieval and can be used for supporting data exploration and analysis tasks. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.

Original languageEnglish (US)
Article number7
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume14
Issue number1
DOIs
StatePublished - Jan 1 2018

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Time series
Data structures
Experiments

Keywords

  • Multi-variate time series
  • Robust multi-variate temporal features

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Robust multi-Variate temporal features of multi-Variate time series. / Liu, Sicong; Poccia, Silvestro Roberto; Candan, Kasim; Sapino, Maria Luisa; Wang, Xiaolan.

In: ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 14, No. 1, 7, 01.01.2018.

Research output: Contribution to journalArticle

Liu, Sicong ; Poccia, Silvestro Roberto ; Candan, Kasim ; Sapino, Maria Luisa ; Wang, Xiaolan. / Robust multi-Variate temporal features of multi-Variate time series. In: ACM Transactions on Multimedia Computing, Communications and Applications. 2018 ; Vol. 14, No. 1.
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