Minimum orders in auto_arima

[dilwong]

Minimum orders in auto_arima

[Relor91]

I have the same issue, it is driving me mad. I am trying to figure it out in the code, will let you know if I manage to fix it

[Relor91]

I found how to deal with it, but beware, this is not an official solution by the people who made the original API, I made it to make my work easier. Also I am not sure if it will damage the overall Stepwise search or anything else, nevertheless if you don't use Stepwise, for now it does seem to work:
Go to the auto.py file by clicking on auto_arima, scroll down to "search = solvers._RandomFitWrapper", you will need to add the below to this function:
start_p=start_p,
start_q=start_q,
start_P=start_P,
start_Q=start_Q
Save it, now go to the _auto_solvers.py and search for the function "class _RandomFitWrapper(_SolverMixin):"
Substitute this function with the below:
class _RandomFitWrapper(_SolverMixin):
"""Searches for the best model using a random search"""

def __init__(self, y, X, fit_partial, d, D, m, max_order,
             start_p, max_p, start_q, max_q, start_P, max_P, start_Q, max_Q, # added start_p, start_q, start_P, start_Q
             random, random_state,
             n_fits, n_jobs, seasonal, trace, with_intercept,
             sarimax_kwargs):

    if seasonal:
        gen = (
            ((p, d, q), (P, D, Q, m))
            for p in range(start_p, max_p + 1)  # changed from 0 to start_p
            for q in range(start_q, max_q + 1)  # changed from 0 to start_q
            for P in range(start_P, max_P + 1)  # changed from 0 to start_P
            for Q in range(start_Q, max_Q + 1)  # changed from 0 to start_Q
            #if p + q + P + Q <= max_order
        )
    else:
        gen = (
            ((p, d, q), (0, 0, 0, 0))
            for p in range(start_p, max_p + 1)  # changed from 0 to start_p
            for q in range(start_q, max_q + 1)  # changed from 0 to start_q
            #if p + q <= max_order
        )

    if random:
        random_state = check_random_state(random_state)
        gen = random_state.permutation(np.array(list(gen), dtype='object'))[:n_fits]

    self.gen = gen
    self.n_jobs = n_jobs
    self.trace = trace

    self.fit_partial = functools.partial(
        fit_partial,
        y=y,
        X=X,
        with_intercept=with_intercept,
        **sarimax_kwargs,
    )

Bosh, save and restart your kernel, now the autoarima will respect the start parameters. I also just tried the Stepwise, and confirm that this amateur fix doesn't work when using Stepwise.