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Modeling Monthly Average Temperature of Dhahran City of Saudi-Arabia Using Arima Models

Received: 30 August 2017     Accepted: 19 September 2017     Published: 1 November 2017
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Abstract

Temperature is the coldness and hotness of the body and its unit is measured in Celsius. The data used for this research work is the average monthly temperature of Dhahran city which is located in the Kingdom of Saudi Arabia. The data range is from 1951 to 2010, and sample data of 1951 to 2008 was used for the estimation to choose the best model and the sample data from 2009 through 2010 was left for the forecast. Different models were tried but ARIMA (2, 1, 1) (0, 1, 1)12 is selected as the best model because of its low sic and aic criteria and also the forecast error, the best model is used for forecasting.

Published in International Journal of Data Science and Analysis (Volume 3, Issue 5)
DOI 10.11648/j.ijdsa.20170305.12
Page(s) 40-45
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

ARMA, ARIMA AR, MA, SMA

References
[1] Nicholls N, Collins D. 2006. Observed climate change in Australia over the past century. Energy & Environment 17(1): 1–12.
[2] Folland CK, Karl TR, Christy JR, Clark RA, Gruza GV, Jouzel J, Mann ME, Oerlemans J, Salinger MJ, Wang SW. 2001. Observed Climate Variability and Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Johnson CA (eds). Cambridge University Press: Cambridge; 99–181.
[3] Karl TR, Janes PD, Knight RW, Kukla J, Plummer N, Razuvayev V, Gallo KP, Lindesay J, Charlson RJ, Peterson TC. 1993. A symmetric trends of daily maximum and minimum temperatures: empirical evidence and possible causes. Bulletin of the American Mathematical Society 74: 1007–1023.
[4] Nury, A. H., Koch, M. & Alam, M. J. B. (2013), Time Series Analysis and Forecasting of Temperatures in Sylhet Division of Bangladesh, 4th International Conference on Environmental Aspects of Bangladesh (ICEAB), August 24-26.
[5] Buishand JA. 1982. some methods for testing the homogeneity of rainfall records, 11-57 pp.
[6] Mitchell JM. 1966. Climatic Change, Technical note 79, WMO No. 195, 2-5, 60 pp. Rasouli AA. 2003. Preliminary time series analysis of Tabriz air temperature, IRIMO, NIVAR No. 46, 47, 16-20 pp.
[7] Elagib NA, Addin Abdu AS. 1997. Climate variability and aridity in Bahrain. Journal of Arid Environment 36: 405–419.
[8] Turkes M, Sumer UM, Demir I. 2002. Re-evaluation of trends and changes in mean, maximum and minimum temperatures of Turkey for the period 1929–1999. International Journal of Climatology 22: 947–977.
[9] Box, G. E. P. & Jenkins, G. M. (1976), Time Series Analysis: Forecasting and Control. Revised Edition, Holden Day: San Francisco, CA.
[10] Olufemi, S. O., Femi, J. A. & Oluwatosin, T. D. (2010), Time Series Analysis of Rainfall and Temperature in South West Nigeria, the Pacific Journal of Science and Technology, Vol. 11 No. 2 pp 552-564.
Cite This Article
  • APA Style

    Nurudeen Ayobami Ajadi, Jimoh Olawale Ajadi, Adams Saddam Damisa, Osebekwen Ebenezer Asiribo, Ganiyu Abayomi Dawodu. (2017). Modeling Monthly Average Temperature of Dhahran City of Saudi-Arabia Using Arima Models. International Journal of Data Science and Analysis, 3(5), 40-45. https://doi.org/10.11648/j.ijdsa.20170305.12

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    ACS Style

    Nurudeen Ayobami Ajadi; Jimoh Olawale Ajadi; Adams Saddam Damisa; Osebekwen Ebenezer Asiribo; Ganiyu Abayomi Dawodu. Modeling Monthly Average Temperature of Dhahran City of Saudi-Arabia Using Arima Models. Int. J. Data Sci. Anal. 2017, 3(5), 40-45. doi: 10.11648/j.ijdsa.20170305.12

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    AMA Style

    Nurudeen Ayobami Ajadi, Jimoh Olawale Ajadi, Adams Saddam Damisa, Osebekwen Ebenezer Asiribo, Ganiyu Abayomi Dawodu. Modeling Monthly Average Temperature of Dhahran City of Saudi-Arabia Using Arima Models. Int J Data Sci Anal. 2017;3(5):40-45. doi: 10.11648/j.ijdsa.20170305.12

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  • @article{10.11648/j.ijdsa.20170305.12,
      author = {Nurudeen Ayobami Ajadi and Jimoh Olawale Ajadi and Adams Saddam Damisa and Osebekwen Ebenezer Asiribo and Ganiyu Abayomi Dawodu},
      title = {Modeling Monthly Average Temperature of Dhahran City of Saudi-Arabia Using Arima Models},
      journal = {International Journal of Data Science and Analysis},
      volume = {3},
      number = {5},
      pages = {40-45},
      doi = {10.11648/j.ijdsa.20170305.12},
      url = {https://doi.org/10.11648/j.ijdsa.20170305.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20170305.12},
      abstract = {Temperature is the coldness and hotness of the body and its unit is measured in Celsius. The data used for this research work is the average monthly temperature of Dhahran city which is located in the Kingdom of Saudi Arabia. The data range is from 1951 to 2010, and sample data of 1951 to 2008 was used for the estimation to choose the best model and the sample data from 2009 through 2010 was left for the forecast. Different models were tried but ARIMA (2, 1, 1) (0, 1, 1)12 is selected as the best model because of its low sic and aic criteria and also the forecast error, the best model is used for forecasting.},
     year = {2017}
    }
    

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    AU  - Jimoh Olawale Ajadi
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    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    AB  - Temperature is the coldness and hotness of the body and its unit is measured in Celsius. The data used for this research work is the average monthly temperature of Dhahran city which is located in the Kingdom of Saudi Arabia. The data range is from 1951 to 2010, and sample data of 1951 to 2008 was used for the estimation to choose the best model and the sample data from 2009 through 2010 was left for the forecast. Different models were tried but ARIMA (2, 1, 1) (0, 1, 1)12 is selected as the best model because of its low sic and aic criteria and also the forecast error, the best model is used for forecasting.
    VL  - 3
    IS  - 5
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Author Information
  • Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria

  • Department of Mathematics and Statistics, King Fahad University of Petroleum and Minerals, Dhahran, Saudi Arabia

  • Department of Statistics, Ahmadu Bello University, Zaria, Nigeria

  • Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria

  • Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria

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