Call for Paper

CAE solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 30, 2023.

Read More

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

Smail Tigani, Mohamed Ouzzif, Rachid Saadane Published in Algorithms

Communications on Applied Electronics
Year of Publication: 2015
© 2015 by CAE Journal

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

	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}


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.


  1. M. M. Mohie El-Din, Y. Abdel-Aty, A. R. Shafay, Two sample Bayesian prediction intervals for order statistics based on the inverse exponential-type distributions using right censored sample, Journal of the Egyptian Mathematical Society, Vol. 19, No. 3, 2011. pp 102–105.
  2. M. Hossain, Y. Muromachi, A real-time crash prediction model for the ramp vicinities of urban expressways, IATSS Research, Vol. 37, No. 1, 2013, pp. 68–79.
  3. D. Ying Ying Sim,C. SiongTeh,P. Kumar Banerjee, Prediction model by using Bayesian and cognition-driven techniques: astudy in the context of obstructive sleep apnea, The 9th International Conference on Cognitive Science, 2013, pp. 528–537.
  4. C. -J. Cheng, S. W. Chiu, C. -B. Cheng, J. -Y. Wu, Customer lifetime value prediction by a Markov chain based data mining model: Application to an auto repair and maintenance company in Taiwan, Scientia Iranica, Transactions E: Industrial Engineering,, Vol. 19, No. 3, 2012. pp 849–855.
  5. M. Cavers, K. Vasudevan, Spatio-Temporal Complex Markov Chain (SCMC) Model Using Directed Graphs: Earthquake Sequencing, Pure and Applied Geophysics, DOI 10. 1007/s00024-014-0850-7, 2014.
  6. D. Xiong, R. Liu, F. Xiao, and X. Gao, ProMT: Effective Human Promoter Prediction Using Markov Chain Model Based on DNA Structural Properties, IEEE Transactions on NanoBioscience, DOI : 10. 1109/TNB. 2014. 2327586, 2013.
  7. D. Ying Ying Sim, C. SiongTeh,P. Kumar Banerjee, Wind Speed and Direction Prediction for Wind Farms Using Support Vector Regression, The fifth International Renewable Energy Congress IREC, 2014, March 25 - 27, Hammamet, TUNISIA.
  8. J. Guo-xun, X. Sheng-ming, H. Xian-wei, L. Chuang-qi, Research on the Prediction of Gas Emission Quantity in Coal Mine Based on Grey System and Linear Regression for One Element, First International Symposium on Mine Safety Science and Engineering, pp 1585–1590, 2011.
  9. S. Tigani, M. Ouzzif, A. Hasbi, Monte Carlo Simulation based Algorithm Design for Automatic Learning Machine Performance Analysis, Fifth International Conference on Next Generation Networks and Services (NGNS) 28-30 May 2014, Casablanca, Morocco.
  10. Y. Lv, Y. Duan, W. Kang, Z. Li, F. -Y. Wang, Traffic Flow PredictionWith Big Data: A Deep Learning Approach, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol. PP, No. 99, 2011. pp 1–9.
  11. K. Slavakis, Georgios B. Giannakis, and G. Mateos, Modeling and Optimization for Big Data Analytics, IEEE SIGNAL PROCESSING MAGAZINE, Vol. PP, No. 99, September 2014. pp 18–31.


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