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Computational Intelligence Techniques in Earth and Environmental Sciences pp 79–91Cite as

Time Series Model Building and Forecasting on Maximum Temperature Data

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Abstract

Temperature is one of the factors of climate variables and understanding its nature is very important because the effect of temperature on climate change is higher than that of other variables. The purpose of the present study was to build an appropriate model to forecast the monthly maximum temperature of Rajshahi district in Bangladesh. The Box–Jenkins modeling strategy was performed using EViews software. This strategy was performed using the Augmented Dickey–Fuller, Phillip–Perron, Kwiatkowski–Phillips–Schmidt–Shin, autocorrelation function, partial autocorrelation function, ordinary least square method, normal P–P plot, Chow’s breakdown test, Chow’s forecast test, and standardized residuals plot. Seasonal variation, cyclical variation, and a slightly upward trend over time were found in the temperature. The temperature was found to be stationary at level after removing the cyclical variation using the resistant smoothing method, 4253H-twice. The SARMA(2, 1)(1, 2)12 model was found to be the most appropriate model for forecasting. The fitted model is also stable with no structural change and thus applicable for forecasting and policy purposes. Finally, this model was used for forecasting maximum temperature from January 2010 to December 2015. The forecasted value divulged that the maximum temperature will be increased by 3 °C during 2010–2015. This is an alarming situation for the environment and should take initiative to control and save our environment of Rajshahi district in Bangladesh.

Keywords

  • Box–Jenkins modeling strategy
  • Correlogram
  • Normal P–P plot
  • Stability test
  • Unit root test

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Acknowledgements

We would like to express our gratitude to Professor Dr. Mohammed Nasser, Chairman, Department of Statistics, University of Rajshahi, Bangladesh for giving us the opportunity to present this chapter in the International Conference and publish in Conference Proceedings. A very special thank goes to Dr. Tanvir Islam, Department of Civil Engineering, University of Bristol, and to his honorable team for publishing this manuscript with a revised version again as a Book Chapter of Springer Publication.

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Authors and Affiliations

  1. Department of Mathematics, Washington State University, Pullman, WA, 99164, USA

    Amrina Ferdous

  2. Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Amrina Ferdous, Md. Abu Shahin & Md. Ayub Ali

Authors
  1. Amrina Ferdous
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  2. Md. Abu Shahin
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  3. Md. Ayub Ali
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Corresponding author

Correspondence to Amrina Ferdous .

Editor information

Editors and Affiliations

  1. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA, NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA, Fort Collins, CO, USA

    Tanvir Islam

  2. NASA Goddard Space Flight Center, Greenbelt & ESSIC, University of Maryland College Park, MD, USA, Greenbelt, Maryland, USA

    Prashant K. Srivastava

  3. Department of Civil Engineering, IIT Delhi, Delhi, India

    Manika Gupta

  4. School of Geog. and Env. Sciences, Monash University, Melbourne, Australia

    Xuan Zhu

  5. School of Environmental Sciences Remote Sensing, Jawaharlal Nehru University, New Delhi, India

    Saumitra Mukherjee

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Ferdous, A., Shahin, M.A., Ali, M.A. (2014). Time Series Model Building and Forecasting on Maximum Temperature Data. In: Islam, T., Srivastava, P., Gupta, M., Zhu, X., Mukherjee, S. (eds) Computational Intelligence Techniques in Earth and Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8642-3_4

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