Which DTW Method Applied to Marine Univariate Time Series Imputation

Conference object, Other literature type English OPEN
Phan , Thi-Thu-Hong ; Caillault , Émilie ; Lefebvre , Alain ; Bigand , André (2017)
  • Publisher: HAL CCSD
  • Related identifiers: doi: 10.1109/OCEANSE.2017.8084598
  • Subject: Similarity measures | Adaptive Feature Based DTW (AF- BDTW) | Adaptive Feature Based DTW (AF-BDTW) | Dynamic Time Warping-D (DTW-D) | Univariate time series | [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML] | Dynamic Time Warping (DTW) | Derivative DTW (DDTW) | Missing data

International audience; Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imputation of marine univariate time series. In the first step, the missing data are completed on various mono-dimensional time series. To fill a missing sub-sequence (gap) in a time series, we first find the most similar sub-sequence to the sub-sequence before (resp. after) this gap according a Dynamic Time Warping (DTW)-cost. Then we complete the gap by the next (resp. previous) sub-sequence of the most similar one. Through experiments results on 5 different datasets we conclude that i) DTW gives the best results when considering the accuracy of imputation values and ii) Adaptive Feature Based DTW (AFBDTW) metric yields very similar shape of imputation values similar to the one of true values.
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