TITLE:
PROFHMM_UNC: Introducing a Priori Knowledge for Completing Missing Values of Multidimensional Time-Series
AUTHORS:
A. A. Charantonis, F. Badran, S. Thiria
KEYWORDS:
Multidimensional Time-Series Completion, Hidden Markov Models, Self-Organizing Maps
JOURNAL NAME:
International Journal of Communications, Network and System Sciences,
Vol.7 No.8,
August
19,
2014
ABSTRACT:
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.