TY - GEN
T1 - Leveraging metadata for identifying local, robust multi-variate temporal (RMT) features
AU - Wang, Xiaolan
AU - Candan, Kasim
AU - Sapino, Maria Luisa
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Many applications generate and/or consume multi-variate temporal data, yet experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this paper, 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, known a priori. Relying on these observations, we develop algorithms to detect robust multi-variate temporal (RMT) features which can be indexed for efficient and accurate retrieval and can be used for supporting analysis tasks, such as classification. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.
AB - Many applications generate and/or consume multi-variate temporal data, yet experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this paper, 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, known a priori. Relying on these observations, we develop algorithms to detect robust multi-variate temporal (RMT) features which can be indexed for efficient and accurate retrieval and can be used for supporting analysis tasks, such as classification. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.
UR - http://www.scopus.com/inward/record.url?scp=84901764871&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84901764871&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2014.6816667
DO - 10.1109/ICDE.2014.6816667
M3 - Conference contribution
AN - SCOPUS:84901764871
SN - 9781479925544
T3 - Proceedings - International Conference on Data Engineering
SP - 388
EP - 399
BT - 2014 IEEE 30th International Conference on Data Engineering, ICDE 2014
PB - IEEE Computer Society
T2 - 30th IEEE International Conference on Data Engineering, ICDE 2014
Y2 - 31 March 2014 through 4 April 2014
ER -