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Reseach Article

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

by Smail Tigani, Mohamed Ouzzif, Rachid Saadane
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 5
Year of Publication: 2015
Authors: Smail Tigani, Mohamed Ouzzif, Rachid Saadane
10.5120/cae-1547

Smail Tigani, Mohamed Ouzzif, Rachid Saadane . Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations. Communications on Applied Electronics. 1, 5 ( April 2015), 1-4. DOI=10.5120/cae-1547

@article{ 10.5120/cae-1547,
author = { Smail Tigani, Mohamed Ouzzif, Rachid Saadane },
title = { Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations },
journal = { Communications on Applied Electronics },
issue_date = { April 2015 },
volume = { 1 },
number = { 5 },
month = { April },
year = { 2015 },
issn = { 2394-4714 },
pages = { 1-4 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume1/number5/326-1547/ },
doi = { 10.5120/cae-1547 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T18:37:36.238340+05:30
%A Smail Tigani
%A Mohamed Ouzzif
%A Rachid Saadane
%T Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations
%J Communications on Applied Electronics
%@ 2394-4714
%V 1
%N 5
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, 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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

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