figure - auto arima similar to our findings using acf-pacfauto arima suggests that the best fitting model is arima( , , based upon the aic criteria note that aic or akaike information criterion measures the goodness of fit and parsimony it is relative metric and does not point toward quality of models in an absolute sensei if all models being compared are pooraic will not be able to point that out thusaic should be used as heuristic low value points toward better fitting model the following is the summary generated by the arima( , , fitsee figure - figure - summary of arima( , , the summary is quite self-explanatory the top section shows details about the training sampleaicand other metrics the middle section talks about the coefficients of the fitted mode in case of arima( , , for iteration both ar and ma coefficients are statistically significant the forecast plot for iteration with arima( , , fitted is shown in figure -