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Could you open an issue for this?

@Kevin Kho I've found that a similar issue has already been reported https://github.com/Nixtla/statsforecast/issues/152 However it looks like he just set

stepwise=False

(which in my case works fine until I also set

max_q=0

)

Ah ok. Thanks!

What about setting max_q to None?

I think it will still error because the loop is not passed here

It seems to work, should I expect any error?

No you shouldn’t. That’s equivalent to q=0 right?

@Kevin Kho I think the problem is that the fit is not executed when max_p or max_q or max_P or max_Q are set to zero. Maybe we should change the code to:

for i in range(max_p + 1):
        for j in range(max_q + 1):
            for I in range(max_P + 1):
                for J in range(max_Q + 1):
                    if i + j + I + J > max_order:

So that the fit is executed at least 1 time even when one of those parameters is set to zero.

I think that would add extra runs right?

I think it's an error introduced when you ported code from R. This is the auto.arima code:

for (i in 0:max.p) {
      for (j in 0:max.q) {
        for (I in 0:max.P) {
          for (J in 0:max.Q) {

:max.p etc. is inclusive, so you have to add 1 to get the same behavior in Python

I believe you, but I don’t work on the models so I need someone else to chime in 😂 . I am more on the Spark, Dask, Ray side and distributed computing.

So basically there's also the problem that max_p, max_q etc. values are never tried (because range()'s stop value is not inclusive)

Yeah

I created a pull request to fix this

Thanks! Will take a look in a bit

Hi! I'll take a look at this issue. Thanks for bringing this up 🙂