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Autoregressive Models

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Informal Introduction to Stochastic Processes with Maple

Part of the book series: Universitext ((UTX))

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Abstract

We investigate processes having the following generalized Markovian property: to forecast their future, we need only the last k consecutive observations; a more distant past becomes irrelevant. We also discuss the issue of parameter estimation (unique to this chapter). The resulting theory can be applied to model random daily fluctuations (but not systematic or seasonal trends) of a stock market and similar situations.

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References

  1. M. S. Bartlett. An Introduction to Stochastic Processes, with Special Reference to Methods and Applications. Cambridge University Press, Cambridge/New York, 1980.

    Google Scholar 

  2. U. Narayan Bhat and G. K. Miller. Elements of Applied Stochastic Processes. Wiley-Interscience, Hoboken, N.J. 2002.

    Google Scholar 

  3. R. Bronson. Schaum’s Outline of Theory and Problems of Matrix Operations. McGraw-Hill, New York, 1989.

    Google Scholar 

  4. W. Feller. An Introduction to Probability Theory and Its Applications. Wiley, New York, 1968.

    Google Scholar 

  5. S. Goldberg. Introduction to Difference Equations. Dover, New York, 1986.

    Google Scholar 

  6. S. Karlin. A First Course in Stochastic Processes. Academic, New York, 1975.

    Google Scholar 

  7. S. Karlin and H. M. Taylor. A Second Course in Stochastic Processes. Academic, New York, 1981.

    Google Scholar 

  8. J. G. Kemeny and J. L. Snell. Finite Markov Chains. Springer, New York, 1976.

    Google Scholar 

  9. J. Medhi. Stochastic Processes. Wiley, New York, 1994.

    Google Scholar 

  10. J. Medhi. Stochastic Models in Queueing Theory. Academic, Amsterdam, 2003.

    Google Scholar 

  11. S. Ross. Stochastic Processes. Wiley, New York, 1996.

    Google Scholar 

  12. A. Stuart. Kendall’s Advanced Theory of Statistics. Wiley, Chichester, 1994.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics, Brock University, St Catharines, Ontario, Canada

    Jan Vrbik

  2. Department of Computer Science, The University of Western Ontario, London, Ontario, Canada

    Paul Vrbik

Authors
  1. Jan Vrbik
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  2. Paul Vrbik
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Vrbik, J., Vrbik, P. (2013). Autoregressive Models. In: Informal Introduction to Stochastic Processes with Maple. Universitext. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4057-4_11

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