CODEWITHSUNDEEP
pythonstatsmodels
Zanam
Zanam

Reputation: 4807

Using Python ARMA model fit

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.

Upvotes: 1

Views: 1158

Answers (2)

Jean A.
Jean A.

Reputation: 301

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.

Upvotes: 0

Dharik
Dharik

Reputation: 36

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

From Rob's book chapter 8.3,

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

From Rob's book chapter 8.4,

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.

Upvotes: 1

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