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Thèses >

Elastic matching for classification and modelisation of incomplete time series

Hong Phan

vendredi 12 octobre 2018 à 14h00

salle B014

Missing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and multivariate time series. We begin by investigating an imputation approach to overcome these issues in univariate time series. This approach is based on the combination of feature-extraction algorithm and Dynamic Time Warping method. A new R package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un- correlated multivariate time series (called DTWUMI). Imputation for this type of data has received little attention in the literature. A key issue of the two proposed approaches is that using the elastic matching to estimate completion values makes it as much as possible to consider the dynamics and the shape of knowledge data, while applying the feature-extraction algorithm allows to reduce the computing time. Successively, we introduce a new approach for filling successive missing values in low/un- correlated multivariate time series which permits to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy similarity based on fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database, thus (ii) forecast various meteorological univariate time series in Vietnam.