This work presents an auto-configurable algorithm for finite state prediction. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before final decision. The auto-rectification mechanism is based on two parallel mathematical models : a Markov chain model for next state prediction rectified with a linear regression model for residues forecasting. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. This work proposes also some statistical key performance indicators in order to prove the efficiency of the approach. Simulation results shows the advantages of the proposed algorithm compared with the traditional one.

Analysis of Arabic language has become a necessity because of its big evolution