(1) . Therefore, only q errors will affect the existing level, but higher order errors do not affect . This indicates that it is a short memory model. Auto-Regression (AR) p, an AR ( (2) The model is described in terms of past values and therefore we would like to estimate the coefficients , and use the model for forecasting. All previous values will have cumulative effects on the existing level, which is a long-run memory model. Autoregressive Integrated Moving Average (ARIMA) Process ARIMA modeling methods were used in this study based on a common method available for modeling and forecasting the time series data. ARIMA is the most common class of time series models which can be made “stationary” by differencing (if necessary), possibly in combination with non-linear transformations such as logging or deflating (if necessary)