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| Phan, Thi-thu-hong; Poisson Caillault, Émilie Poisson; Lefebvre, Alain; Bigand, André. |
| 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... |
| Tipo: Text |
Palavras-chave: Imputation; Missing data; Univariate time series; DTW; Similarity. |
| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00396/50696/51387.pdf |
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