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Modeling an Average Monthly Temperature of Sokoto Metropolis Using Short Term Memory Models

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  • Musa Y.

Abstract

In this paper, the results of seasonal modeling of Sokoto monthly average temperature have been obtained using seasonal autoregressive integrated moving average modeling approach. Based on this seasonal modeling analysis, we conclude that, the best seasonal model among the models that are adequate to describe the seasonal dynamics for Sokoto city temperature is SARIMA (3,0,1)(4,1,0) 12, SARIMA (1,0,0)(0,1,1) 12 and SARIMA (4,0,2)(5,1,1) 12 models. These models are the only models that passed all the diagnostic tests and thus it can be used for forecasting at some future time.

Suggested Citation

  • Musa Y., 2014. "Modeling an Average Monthly Temperature of Sokoto Metropolis Using Short Term Memory Models," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(7), pages 382-397, July.
    • Handle: RePEc:hur:ijarbs:v:4:y:2014:i:7:p:382-397
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    References listed on IDEAS

    as
    1. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
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    More about this item

    Keywords

    Seasonality; SARIMA; Identification; Estimation; and Diagnostics test;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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