Abstract: The use of statistical methods to study and predict changes in climate parameters has found wide application. One of these is Autoregressive Integrated Moving Average (ARIMA) model. In this study, the average monthly relative humidity of Pars Abad-e-Moghan station investigated based on above-mentioned model in a 25 years statistical period (1984-2010). Time series plot was used to investigate whether the series is stationary or non-stationary. Because the series was not stationary, so it was converted to a stationary series by Differencing order 1 (D = 1). Functions such as Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Akaikes Information Criterion (AIC) were calculated to determine the order of Autoregressive (AR (p)) and Moving Average (MA (q)) parameters (Q, q, P, p). With considering above-mentioned criteria, seasonal ARIMA (p = 1, d = 0, q = 1) (P = 1, D = 1, Q = 1) modal was chosen as best modal. So, it was used to predict future values of relative humidity. Before using the model for forecasting, it checked for adequacy based on plot of standardized residuals, ACF plot of residuals, PACF plot of residuals. The relative humidity of Pars Abad-e-Moghan station was forecasted based on selected modal up to 2014. The results of which indicate the relative humidity in May, June and September will be faced with an increasing trend while in the rest of months will be not observed any trend.
Ghita Shiri, Bromand Salahi, Rasool Samadzadeh and Mohammadreza Shiri, 2011. The Investigation and Forecasting of Relative Humidity Variation of Pars Abad-e-Moghan, North-West of Iran, by ARIMA Model. Research Journal of Applied Sciences, 6: 81-87.