  This work aims at providing a new model for time series classification based
on learning from just one example. We assume that time series can be well
characterized as a parametric random process, a sort of Hidden semi-Markov
Model representing a sequence of regression models with variable duration. We
introduce a parametric stochastic model for time series pattern recognition and
provide a maximum-likelihood estimation of its parameters. Particularly, we are
interested in examining two different representations for state duration: i) a
discrete density distribution requiring an estimate for each possible duration;
and ii) a parametric family of continuous density functions, here the Gamma
distribution, with just two parameters to estimate. An application on heartbeat
classification reveals the main strengths and weaknesses of each alternative.
