PROFHMM_UNC: Introducing a Priori Knowledge for Completing Missing Values of Multidimensional Time-Series


We present a new method for estimating missing values or correcting unreliable observed values of time dependent physical fields. This method, is based on Hidden Markov Models and Self-Organizing Maps, and is named PROFHMM_UNC. PROFHMM_UNC combines the knowledge of the physical process under study provided by an already known dynamic model and the truncated time series of observations of the phenomenon. In order to generate the states of the Hidden Markov Model, Self-Organizing Maps are used to discretize the available data. We make a modification to the Viterbi algorithm that forces the algorithm to take into account a priori information on the quality of the observed data when selecting the optimum reconstruction. The validity of PROFHMM_UNC was endorsed by performing a twin experiment with the outputs of the ocean biogeochemical NEMO-PISCES model.

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Charantonis, A. , Badran, F. and Thiria, S. (2014) PROFHMM_UNC: Introducing a Priori Knowledge for Completing Missing Values of Multidimensional Time-Series. International Journal of Communications, Network and System Sciences, 7, 316-329. doi: 10.4236/ijcns.2014.78034.

Conflicts of Interest

The authors declare no conflicts of interest.


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