TY - CPAPER T1 - DTW-Approach for uncorrelated multivariate time series imputation A1 - Phan,Thi-Thu-Hong A1 - Poisson Caillault,Emilie A1 - Bigand,Andre A1 - Lefebvre,Alain AD - Univ Littoral Cote dOpale, EA LISIC 4491, F-62228 Calais, France. AD - Vietnam Natl Univ Agr, Dept Comp Sci Hanoi, Hanoi, Vietnam. AD - IFREMER, LER BL, F-62321 Boulogne Sur Mer, France. UR - https://doi.org/10.1109/MLSP.2017.8168165 DO - 10.1109/MLSP.2017.8168165 KW - Imputation KW - Uncorrelated multivariate time series KW - Missing data KW - Dynamic Time Warping KW - Similarity measures N2 - Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper, we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of recurrent data. This method involves two main steps. Firstly, we find the most similar sub-sequence to the sub-sequence before (resp.after) a gap based on the shape-features extraction and Dynamic Time Warping algorithms. Secondly, we fill in the gap by the next (resp.previous) sub-sequence of the most similar one on the signal containing missing values. Experimental results show that our approach performs better than several related methods in case of multivariate time series having low/non-correlations and effective information on each signal. Y1 - 2017 CY - 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Int House Japan, Tokyo, JAPAN, SEP 25-28, 2017 SO - Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). 2017. 6 p. ID - 54082 ER -