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During the last years, a number of methodological papers on models with discrete parameter shifts have revived interest in the so-called regime switching models. Piecewise linear models are attractive when modelling a wide range of nonlinear system and determining simultaneously i) the data partition ii) the time instant of change iii) the parameter values of the different local models. This is a difficult problem for which no solution exists in the general case and we show here some aspects and particular results concerning the problem of off line learning of switching time series. We propose a method for identifying the parameters of the local models when choosing an adapted weighting function, this function allowing to select the data for which each local model is active. Indeed the proposed method is able to solve simultaneously the data allocation and the parameter estimation. The feasibility and the performance of the procedure is demonstrated using several academic examples.