2014
DOI: 10.1007/s10115-014-0784-5
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Classification of multivariate time series via temporal abstraction and time intervals mining

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Cited by 89 publications
(76 citation statements)
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“…The increased attention to the subject of mining time intervals has led several research groups to quite simultaneously propose using the discovered temporal patterns as features for classifying multivariate time series (Patel et al 2008;Batal et al 2012Batal et al , 2013, including the suggestion of a highly preliminary version of the KarmaLegoS framework Moskovitch and Shahar 2014). Interestingly, all of the studies that reported the use of temporal abstraction and time intervals mining for the purpose of classification were using datasets from the biomedical domain (Patel et al 2008;Batal et al 2012Batal et al , 2013.…”
Section: Classification Via Frequent Patternsmentioning
confidence: 98%
“…The increased attention to the subject of mining time intervals has led several research groups to quite simultaneously propose using the discovered temporal patterns as features for classifying multivariate time series (Patel et al 2008;Batal et al 2012Batal et al , 2013, including the suggestion of a highly preliminary version of the KarmaLegoS framework Moskovitch and Shahar 2014). Interestingly, all of the studies that reported the use of temporal abstraction and time intervals mining for the purpose of classification were using datasets from the biomedical domain (Patel et al 2008;Batal et al 2012Batal et al , 2013.…”
Section: Classification Via Frequent Patternsmentioning
confidence: 98%
“…However, it is also possible to abstract point-based data by applying temporal knowledge which results in a more abstract representation of the data, in the form of symbolic time intervals Batal et al provide several pattern mining techniques that uses a time interval-related representation of a sequence, which requires either the events have continuous values that can be quantized or the duration of every event is available [36,42]. Moskovitch et al provide several approaches for discretizing continuous event values to derive more discriminative time-interval related patterns [40,41]. Patel et al also provide a technique for mining interval-based events [43].…”
Section: Background and Significancementioning
confidence: 99%
“…Some of these intervalbased methods have been specifically extended to handle classification and prediction tasks, in particular, in clinical domains [38].…”
Section: Figurementioning
confidence: 99%