Evolutionary Based ARIMA Models for Stock Price Forecasting

  • Tomas VantuchEmail author
  • Ivan Zelinka
Part of the Emergence, Complexity and Computation book series (ECC, volume 14)


Time series prediction is mostly based on computing future values by the time set past behavior. If the prediction like this is met with a reality, we can say that the time set has a memory, otherwise the new values of time set are not affected by its past values. In the second case we can say, there is no memory in the time set and it is pure randomness. In a faith of ”market memory”, the stock prices are often studied, analyzed and forecasted by a statistic, an econometric, a computer science... In this article the econometric ARIMA model is taken for previously mentioned purpose and its constructing and estimation is modified by evolution algorithms. The algorithms are genetic algorithm (GA) and particle swarm optimization PSO.


ARIMA GA PSO AIC BIC forecasting 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)zbMATHGoogle Scholar
  2. 2.
    Ljung, G.M., Box, G.E.P.: The likelihood function of stationary autoregressive-moving average models. Biometrika 66(2), 265–270 (1979)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Morf, M., Sidhu, G.S., Kailath, T.: Some new algorithms for recursive estimation on constant linear discrete time systems. IEEE Transactions on Automatic Control 19(4), 315–323 (1974)CrossRefzbMATHGoogle Scholar
  4. 4.
    Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716–723 (1974)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Weakliem, D.L.: A Critique of the Bayesian Information Criterion for Model Selection. Sociological Methods and Research 27(3), 359–397 (1999)CrossRefGoogle Scholar
  6. 6.
    Tasy, R.S., Tiao, G.C.: Use of canonical analysis in time series model identification. Biometrika 72(2), 299–315 (1985)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Tasy, R.S., Tiao, G.C.: Consistent estimates of autoregressive parameters and extended sample autocorrelation function for stationary and nonstationary ARMA model. Journal of the American Statistical Association 79(1), 84–96 (1984)MathSciNetGoogle Scholar
  8. 8.
    Hannan, E.J., Rissanen, J.: Recursive estimation of mixed autogressive-moving average order. Biometrika 69(1), 81–94 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Hannan, E.J., Quinn, B.G.: The determination of the order of an autoregression. Journal of the Royal Statistical Society B 41(2), 190–195 (1979)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  11. 11.
    Kinnear Jr., K.E. (ed.): Advances in Genetic Programming. MIT Press, Cambridge (1994)Google Scholar
  12. 12.
    Poli, R., Langdon, W.B.: Genetic Programming with One-Point CrossoverGoogle Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, pp. 1942–1948 (November 1995)Google Scholar
  14. 14.
    Kennedy, J.: The particle swarm: Social adaptation of knowledge. In: Proc. 1997 Int. Conf. Evolutionary Computation, Indianapolis, IN, pp. 303–308 (April 1997)Google Scholar
  15. 15.
    White, D.R.: Software review: the ECJ toolkit (March 2012)Google Scholar
  16. 16.
    Dickey, D.G.: Dickey-Fuller Tests. In: International Encyclopedia of Statistical Science, pp. 385–388 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.VSB-Technical University of OstravaOstravaCzech Republic

Personalised recommendations