FN Archimer Export Format
PT J
TI Dynamic Time Warping-based imputation for univariate time series data
BT 
AF Phan, Thi-Thu-Hong
   Poisson Caillault, Émilie
   LEFEBVRE, Alain
   Bigand, André
AS 1:1,2;2:1,3;3:3;4:1;
FF 1:;2:;3:PDG-ODE-LITTORAL-LERBL;4:;
C1 Univ. Littoral Côte d’Opale, EA 4491-LISIC, F-62228 Calais, France
   Vietnam National University of Agriculture, Department of Computer Science, Hanoi, Vietnam
   IFREMER, LER BL, F-62321 Boulogne-sur-mer, France
C2 UNIV LITTORAL COTE D'OPALE, FRANCE
   UNIV NATL VIETNAM, VIETNAM
   IFREMER, FRANCE
SI BOULOGNE
SE PDG-ODE-LITTORAL-LERBL
IN WOS Ifremer UPR
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
   copubli-sud
IF 3.756
TC 30
UR https://archimer.ifremer.fr/doc/00396/50696/51387.pdf
LA English
DT Article
DE ;Imputation;Missing data;Univariate time series;DTW;Similarity
AB 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. 
PY 2020
PD NOV
SO Pattern Recognition Letters
SN 0167-8655
PU Elsevier BV
VL 139
UT 000582633700017
BP 139
EP 147
DI 10.1016/j.patrec.2017.08.019
ID 50696
ER

EF