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;
  • Publisher: Proceedings of Oceans 2017 - Aberdeen Conference. 19-22 June 2017, Aberdeen, England. 7p.
  • 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 ... View more
  • References (36)
    36 references, page 1 of 4

    [1] K. Rousseeuw, E. P. Caillault, A. Lefebvre, and D. Hamad, “Monitoring system of phytoplankton blooms by using unsupervised classifier and time modeling,” in 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS. IEEE, 2013, pp. 3962-3965.

    [2] H.-T. Ceong, H.-J. Kim, and J.-S. Park, “Discovery of and recovery from failure in a costal marine usn service,” Journal of Information and Communication Convergence Engineering, vol. 1, no. 1, Mar 2012. [Online]. Available: http://dx.doi.org/10.6109/jicce.2012.10.1.011

    [3] N. M. Noor, M. M. Al Bakri Abdullah, A. S. Yahaya, and N. A. Ramli, “Comparison of Linear Interpolation Method and Mean Method to Replace the Missing Values in Environmental Data Set,” Materials Science Forum, vol. 803, pp. 278-281, Aug. 2014.

    [4] G. Hawthorne, G. Hawthorne, and P. 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.

    [5] H. Junninen, H. Niska, K. Tuppurainen, J. Ruuskanen, and M. Kolehmainen, “Methods for imputation of missing values in air quality data sets,” Atmospheric Environment, vol. 38, no. 18, pp. 2895- 2907, Jun. 2004.

    [6] J. L. Schafer, Analysis of Incomplete Multivariate Data. CRC Press, Aug. 1997.

    [7] S. Van Buuren, H. C. Boshuizen, D. L. Knook, and others, “Multiple imputation of missing blood pressure covariates in survival analysis,” Statistics in medicine, vol. 18, no. 6, pp. 681-694, 1999.

    [8] T. E. Raghunathan, J. M. Lepkowski, J. Van Hoewyk, and P. Solenberger, “A multivariate technique for multiply imputing missing values using a sequence of regression models,” Survey methodology, vol. 27, no. 1, pp. 85-96, 2001.

    [9] J. Engels and P. Diehr, “Imputation of missing longitudinal data: A comparison of methods,” Journal of Clinical Epidemiology, vol. 56, no. 10, pp. 968-976, Oct. 2003.

    [10] P. Royston, “Multiple imputation of missing values: Further update of ice, with an emphasis on interval censoring,” Stata Journal, vol. 7, no. 4, pp. 445-464, 2007.

  • Metrics
Share - Bookmark