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Computational Intelligence Techniques in Earth and Environmental Sciences pp 29–51Cite as

Vector Autoregression (VAR) Modeling and Forecasting of Temperature, Humidity, and Cloud Coverage

Abstract

Climate change is a global phenomenon but its implications are distinctively local. The climatic variables include temperature, rainfall, humidity, wind speed, cloud coverage, and bright sunshine. The study of behavior of the climatic variables is very important for understanding the future changes among the climatic variables and implementing important policies. The problem is how to study the past, present, and future behaviors of the climatic variables. The purpose of the present study was to develop an appropriate vector autoregression (VAR) model for forecasting monthly temperature, humidity, and cloud coverage of Rajshahi district in Bangladesh. The test for stationarity of the time series variables has been confirmed with augmented Dickey–Fuller, Phillips–Perron, and Kwiatkowski–Phillips–Schmidt–Shin tests. The endogenity among the variables was examined by F-statistic proposed by C.W.J. Granger. The order of the VAR model was selected using Akaike information criterion, Schwarz information criteria, Hannan–Quinn information criteria, final prediction error, and likelihood ratio test. The ordinary least square method was used to estimate the parameters of the model. The VAR(8) model was found to be the best. Structural analyses were performed using forecast error variance decomposition and impulse response function. These structural analyses divulged that the temperature, humidity, and cloud coverage would be interrelated and endogenous in future. Finally, temperature, humidity, and cloud coverage were forecasted from January 2011 to December 2016 using the best selected model VAR(8). The forecasted values showed an upward trend in temperature and humidity and downward trend in cloud coverage. Therefore, we must show our friendly behavior to the environment to control such trends.

Keywords

  • Forecast error variance (FEV) decomposition
  • Granger causality test
  • Impulse response function (IRF)
  • Unit root
  • Vector autoregression (VAR)

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Acknowledgments

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 paper in the International Conference and Publish in Conference Proceedings. Very special thanks go to Dr. Tanvir Islam, Department of Civil Engineering, University of Bristol, and 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 Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh

    Md. Abu Shahin & Md. Ayub Ali

  2. School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD, 4702, Australia

    A. B. M. Shawkat Ali

Authors
  1. Md. Abu Shahin
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  2. Md. Ayub Ali
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  3. A. B. M. Shawkat Ali
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Corresponding author

Correspondence to Md. Abu Shahin .

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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Shahin, M.A., Ali, M.A., Ali, A.B.M.S. (2014). Vector Autoregression (VAR) Modeling and Forecasting of Temperature, Humidity, and Cloud Coverage. 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_2

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