Main Article Content

Arnab Narayan Patowary
Kuldeep Goswami
Jiten Hazarika


The forecasting of temperature on a seasonal time scales has been attempted by many researchers by different techniques at different time across the globe. It is a challenging task to forecast temperature on monthly and seasonal time scale. In this paper, an attempt has been made to develop a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to long term temperature data of Dibrugarh, Assam, for the period of fifty (50) years (1966-2015). The analysis revels that the best seasonal models which are satisfactory to describe the data are SARIMA(2,1,1)(0,1,1)12 for monthly maximum and SARIMA(2,1,1)(0,1,1)12 for monthly minimum temperature data respectively.


Download data is not yet available.

Article Details



Amirpooya S., Arash A., Seyed F. V. 2013 The forecasting of potential evapotranspiration using Time series analysis in humid and semi humid regions. American Journal of Engineering. 2(12), pp. 296-302.

Barthakur, M. 2004 Weather and Climate. In: Singh V.P. The Brahmaputra basin water resources. Kluwer Academic Publishers, Netherlands.

Box, G.E.P. & Jenkins, G.M. 1970 Time Series Analysis: Forecasting and Control. (Revised Edition, 1976), Holden-Day: San Francisco, CA.

Chisimkwuo, J., Uchechukwu, G. And Okezie S.C. 2014 Time series analysis and forecasting of monthly maximum temperatures in south eastern Nigeria. International Journal of Innovative Research and Development. ISSN 2278 – 0211. 3(1), pp. 165-171.

Cryer, J. D. and Chan, K.S. 2008 Time series analysis with application in R. 2nd Edition, Springer, New York, ISBN-10: 0387759581, pp. 491.

Deka, R.L. 2015 Spatio-temporal variability of rainfall regime in the Brahmaputra valley of North East of India. Theory of Applied Climatology, DOI 10.1007/s00704-015-1452-8.

Gallop, C., Tseand, C., Zhao, J. 2012 A Seasonal autoregressive model of vancouver bicycle traffic using weather variables.TRB 2012 Annual Meeting.

Guo, Z.W. 2009 The adjustment method and research progress based on the ARIMA model. Chinese J. Hosp. Stat., 161, pp. 65-69.

Hazarika, J., Pathak, B. and Patowary, A. 2017 Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach. Mausam. 68(2).

He, S.Y. 2004 Applied Time Series Analysis. 1st Edition, Peking University Press, Beijing.

Hu, W., Tong, S., Mengersen, K., and Connell, D. 2007 Weather variability and the incidence of cryptosporidiosis: Comparison of time series Poisson regression and SARIMA Models. Annals of Epidemiology, 17: pp. 679–688.

Kantz, H. and Schreiber, T. 2004 Nonlinear Time Series Analysis. 2nd Edition, Cambridge University Press, Cambridge, ISBN-10: 0521529026, pp. 369.

Kaushik, I. and Singh, S.M. 2008 Seasonal ARIMA Model for forecasting of monthly rainfall and temperature. Journal of Environmental Research and Development. 3(2), pp. 506-514.

Nury, A.H., Koch, M. And 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, pp. 24-26.

Roy, T. D. and Das K. K. 2012 Time series analysis of Dibrugarh air temperature. Journal of Atmospheric and Earth Environment. 1(1), pp. 30-34.

Stain, M. and Lloret, J. 2001 Forecasting of air and water temperatures for fishery purposes with selected examples from Northwest Atlantic. Journal Northwest. Atl. Fishery Science. 29, pp. 23-30.

Stoffer, D.S. and Dhumway, R.H. 2010 Time series analysis and its application. 3rd Edition, Springer,New York, ISBN-10: 1441978658, pp. 596.

Wang, J., Y.H. Du and Zhang, X.T. 2008 Theory and application with seasonal time series. 1st Edition, Nankai University Press, Chinese.