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Robust Multi-Variate Temporal Features of Multi-Variate Time Series

Published:04 January 2018Publication History
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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.

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                    • Published in

                      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
                      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 14, Issue 1
                      February 2018
                      287 pages
                      ISSN:1551-6857
                      EISSN:1551-6865
                      DOI:10.1145/3173554
                      Issue’s Table of Contents

                      Copyright © 2018 ACM

                      Publisher

                      Association for Computing Machinery

                      New York, NY, United States

                      Publication History

                      • Published: 4 January 2018
                      • Accepted: 1 August 2017
                      • Revised: 1 June 2017
                      • Received: 1 February 2017
                      Published in tomm Volume 14, Issue 1

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