Time series analysis and forecasting has become a major tool in different applications in meteorological phenomena, such as rainfall, humidity, temperature, draught and environmental management fields. It has two goals, perception or modeling random mechanism and prediction of future series quantities according to the past. In this research, ARIMA (Auto Regressive Integrated Moving Average) model has been used to carry out short term predictions of monthly rainfall in Sylhet and Moulvibazar district (north-eastern region) for years 2012 to 2014. Based on the inspection of the ACF, PACF autocorrelation plots, the most appropriate orders of the ARIMA models are determined and evaluated using the AIC-criterion. For the monthly rainfall in Sylhet district at Tajpur and Kanairghat station ARIMA (1,1,1) (0,1,1)12 is obtained, whereas the respective models in Moulvibazar district at Chandbagh, Sreemangal and Manu railway bridge are ARIMA (0,1,1) (1,1,1)12, ARIMA (1,1,1) (1,1,1)12 and ARIMA (1,1,0) (0,1,1)12. Among five rainfall stations PBIAS is the least (-1.07%), NSE (88%) and Index of agreement (87%) are the highest at Kanairghat station. Negative Mann-Kendall test statistics of monthly rainfall series (for the period between 1980 and 2011) indicates that monthly rainfall is decreasing with time except Kanairghat station (0.075). Mean rainfall with their standard deviation indicates rainfall is fluctuating with time. The outcomes from this study will assist water engineers and hydrologists to establish strategies, priorities and proper use of water resources in Sylhet and Moulvibazar districts.
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