Dynamic Time Warping-based imputation for univariate time series data

Type Article
Date 2020-11
Language English
Author(s) Phan Thi-Thu-Hong1, 2, Poisson Caillault Émilie1, 3, Lefebvre AlainORCID3, Bigand André1
Affiliation(s) 1 : Univ. Littoral Côte d’Opale, EA 4491-LISIC, F-62228 Calais, France
2 : Vietnam National University of Agriculture, Department of Computer Science, Hanoi, Vietnam
3 : IFREMER, LER BL, F-62321 Boulogne-sur-mer, France
Source Pattern Recognition Letters (0167-8655) (Elsevier BV), 2020-11 , Vol. 139 , P. 139-147
DOI 10.1016/j.patrec.2017.08.019
WOS© Times Cited 30
Keyword(s) Imputation, Missing data, Univariate time series, DTW, Similarity

Time series with missing values occur in almost any domain of applied sciences. Ignoring missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). This paper proposes an approach to fill in large gap(s) within time series data under the assumption of effective information. To obtain the imputation of missing values, we find the most similar sub-sequence to the sub-sequence before (resp. after) the missing values, then complete the gap by the next (resp. previous) sub-sequence of the most similar one. Dynamic Time Warping algorithm is applied to compare sub-sequences, and combined with the shape-feature extraction algorithm for reducing insignificant solutions. Eight well-known and real-world data sets are used for evaluating the performance of the proposed approach in comparison with five other methods on different indicators. The obtained results proved that the performance of our approach is the most robust one in case of time series data having high auto-correlation and cross-correlation, strong seasonality, large gap(s), and complex distribution.

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