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On the Order Determination of Arima Models

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  • T. Ozaki

Abstract

In this paper the difficulty in deciding the order of an arima (autoregressive integrated moving average) model is discussed. The possibility of removing this difficulty by using the maice (minimum aic estimation) procedure, which selects a model by using Akaike's Information Criterion (aic), is checked with the numerical examples treated in the book by Box and Jenkins.

Suggested Citation

  • T. Ozaki, 1977. "On the Order Determination of Arima Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 290-301, November.
    • Handle: RePEc:bla:jorssc:v:26:y:1977:i:3:p:290-301
    DOI: 10.2307/2346970
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    6. Seunghon Ham & Sunju Kim & Naroo Lee & Pilje Kim & Igchun Eom & Byoungcheun Lee & Perng-Jy Tsai & Kiyoung Lee & Chungsik Yoon, 2017. "Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 685-699, March.
    7. Moretti, L. & Polimeni, S. & Meraldi, L. & Raboni, P. & Leva, S. & Manzolini, G., 2019. "Assessing the impact of a two-layer predictive dispatch algorithm on design and operation of off-grid hybrid microgrids," Renewable Energy, Elsevier, vol. 143(C), pages 1439-1453.
    8. Freeman, T.Graham, 1985. "Selecting the best model to fit data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 27(2), pages 137-140.

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