How to fit a SARIMA Model on time series data

ARIMA (Autoregressive Integrated Moving Average) is a major tool used in time series analysis to attempt to forecast future values of a variable based on its present value. For this particular example, a monthly weather dataset from 1941 for Dublin, Ireland from the Irish weather broadcaster Met Eireann is used, and an ARIMA model is constructed to forecast maximum temperature readings using this time series. The SARIMA consists of other forecasting models: AR: Auto regressive model (can be a simple, multiple or non-linear regression) MA: Moving averages model. The composition of AR and MA together do not carry the ARMA model, but this model is used only for stationary series (mean, variance constant over time). The big difference between an ARIMA model and a SARIMA model is the addition of seasonal error components to the model. Remember that the purpose of an ARIMA model is to make the time-series that you are working with act like a stationary series. This is important because if it isn’t sta