• AIC (Akaike Information Criterion)
• BIC (Bayesian Information Criterion)

Equation

$l:\text{likehood, (means how strong the model is to fitting the data)}$ - 愈大愈好

$k:\text{number of parameters}$ - 愈小愈好

$n:\text{number of samples used for fitting}$ - 愈小愈好

$AIC=2k-2l$

$BIC=ln(n) \cdot k -2l$

• Lower AIC means higher log likehood (l) or less parameters (k)
• Lower BIC means higher log likehood (l) or less parameters (k) or less samples used in fitting

Code Reference

https://github.com/ritvikmath/Time-Series-Analysis/blob/master/Model Selection.ipynb

Video Reference

https://www.youtube.com/watch?v=McEN54l3EPU