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Symbolic ARMA Model Analysis

In: Computational Probability

Author

Listed:
  • John H. Drew

    (The College of William and Mary)

  • Diane L. Evans

    (Rose-Hulman Institute of Technology)

  • Andrew G. Glen

    (Colorado College)

  • Lawrence M. Leemis

    (The College of William and Mary)

Abstract

This chapter extends the APPL language to include the analysis of ARMA (autoregressive moving average) time series models. ARMA models provide a parsimonious and flexible mechanism for modeling the evolution of a time series. Some useful measures of these models (e.g., the autocorrelation function or the spectral density function) are oftentimes tedious to compute by hand, and APPL can help ease the computational burden.

Suggested Citation

  • John H. Drew & Diane L. Evans & Andrew G. Glen & Lawrence M. Leemis, 2017. "Symbolic ARMA Model Analysis," International Series in Operations Research & Management Science, in: Computational Probability, edition 2, chapter 11, pages 191-208, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-43323-3_11
    DOI: 10.1007/978-3-319-43323-3_11
    as

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