DTW-Approach for uncorrelated multivariate time series imputation

Conference object, Other literature type English OPEN
Phan, Thi-thu-hong ; Poisson Caillault, Emilie ; Bigand, Andre ; Lefebvre, Alain (2017)
  • Publisher: Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). 2017. 6 p.
  • Related identifiers: doi: 10.1109/MLSP.2017.8168165
  • Subject: Dynamic Time Warping | Similarity measures | Imputation | [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML] | Uncorrelated multivariate time series | Missing data

International audience; 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 t... View more
  • References (3)

    [4] Graeme Hawthorne, Graeme Hawthorne, and Peter Elliott, “Imputing cross-sectional missing data: Comparison of common techniques,” Australian and New Zealand Journal of Psychiatry, vol. 39, no. 7, pp. 583-590, 2005.

    [13] Shah Atiqur Rahman, Yuxiao Huang, Jan Claassen, Nathaniel Heintzman, and Samantha Kleinberg, “Combining Fourier and lagged k -nearest neighbor imputation for biomedical time series data,” Journal of Biomedical Informatics, vol. 58, pp. 198- 207, Dec. 2015.

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