I'm currently trying to make my first ARIMA model. In my limited experience with linear regressions I find that R-squared gives me a pretty good absolute idea of how the model performs. I haven't found anything similar to that with ARIMA models. I know that there is AIC and SBC, but from my understanding they don't mean much by themselves and are used to compare the goodness of fit to other models. Am I correct in my understanding of AIC and SBC? Is there a metric that is similar to R-squared for ARIMA models in that it is a standalone measure of goodness of fit?
Solved – n absolute goodness of fit measure for ARIMA models
aicarimagoodness of fit
Best Answer
$R^2$ is as valid for ARIMA models as it is for linear regression models. In both cases it measures the proportion of the total variance that is explained by the model. It also has the same drawbacks for ARIMA as for linear regression models.
I do not see why goodness of fit should be measured in different ways for ARIMA vs. linear regression, so your question reduces to a more general question of measuring goodness of fit in linear models. That has been discussed in other threads under the tag goodness-of-fit.