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Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations

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Smail Tigani, Mohamed Ouzzif, Rachid Saadane Published in Algorithms

Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations
Communications on Applied Electronics
Year of Publication: 2015
© 2015 by CAE Journal
Series Volume 1 Number 5
Authors Smail Tigani, Mohamed Ouzzif, Rachid Saadane
10.5120/cae-1547
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Smail Tigani, Mohamed Ouzzif and Rachid Saadane. Article: Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations. Communications on Applied Electronics 1(5):1-4, April 2015. Published by Foundation of Computer Science, New York, USA. BibTeX

@article{key:article,
	author = {Smail Tigani and Mohamed Ouzzif and Rachid Saadane},
	title = {Article: Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {1},
	number = {5},
	pages = {1-4},
	month = {April},
	note = {Published by Foundation of Computer Science, New York, USA}
}

Abstract

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.

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Keywords

Auto-configurable Algorithms, Statistical Learning, Stochastic Process, Linear Regression, Performance Analysis.