I have a time series data and I am trying to fit ARMA(p,q) model to it but I am not sure what 'p' and 'q' to use. I came across this link enter link description here
The usage for this model is enter link description here
But I don't think it automatically decides what 'p' and 'q' to use. It seems like I need to know what 'p' and 'q' is appropriate.
You'll have to do a bit of reading outside of the statsmodel package documentation.
See some of the content in this answer: https://stackoverflow.com/a/12361198/6923545
There's a guy named Rob Hyndman who wrote a great book on forecasting and it would be a fine idea to start there. Chapters 8.3 and 8.4 are the bulk of what you're looking for
In an autoregression model, we forecast the variable of interest using a linear combination of past values of the variable.
This is describing p -- the number of past values used to forecast a value
a moving average model uses past forecast errors in a regression-like model.
This is describing q, the number of previous forecast errors in the model.
This link gives you a little bit of theory and some examples.
CASE 1: you already know the values of p and q (orders of the ARMA Model), and the algorithm finds the best coefficients
CASE 2: if you don't know them, you can specify a range of possible values and the algorithme finds the best model ARMA(p,q) that fits to the data and estimates the corresponding coefficients.
