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.
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References
Box GEP, Jenkins GM (1994) Time series analysis: forecasting & control, 3rd edn. Prentice-Hall
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Woodward WA, Gray HL (1981) On the relationship between the S array and the Box–Jenkins method of ARMA model identification. J Am Stat Assoc 76:579–587
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Drew, J.H., Evans, D.L., Glen, A.G., Leemis, L.M. (2017). Symbolic ARMA Model Analysis. In: Computational Probability. International Series in Operations Research & Management Science, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-43323-3_11
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DOI: https://doi.org/10.1007/978-3-319-43323-3_11
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