ACF PACF Determination ARIMA

Question

Please help me confirm my understanding. For below graphs, I believe

AR(p) = 0 and MA(q) = 0

Is that correct?

Answer 1

First, let's learn more...

As Aashiq Reza brought the description link, I think the ACF and PACF plots that you shared is like an MA(2) process. ARIMA(p, i,q) has three elements, p is for AR , i is for difference, and q stands for MA process lag. Because the lag parameter defines the lag in the model's regression formula, if both of p and q be zero, then the model is not ARIMA anymore.

My suggestion: probabilistic model selection...

You can evaluate the correctness of a model for a time-series object using information criteria like AIC and BIC . For example, you have a preset of possible p and q , then you can test each one and get the criteria for it. The model with the least criterion is the best one. This link helps with the calculation in python.