[Model] Arima is getting FloatingPointError about statsforecast HOT 5 CLOSED

Hey @Timuchin. That seems to be due to the default method ('CSS') of the arima function, by setting method='CSS-ML' (the default of statsforecast.models.ARIMA) I get the same result as in statsmodels.

The warnings seem to be coming from the suggestions of the optimization algorithm, which sometimes suggests values that produce Inf in the objective function. I think we can make that return a big float instead.

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Hey @Timuchin, thanks for the excellent report. Are you still getting this error? I get it as a warning, i.e. RuntimeWarning: invalid value encountered in subtract df = fun(x) - f0

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@jmoralez Hello!
After updating statsforecast to 1.7.3 it stopped getting error. But yeah, it throws a warning. I guess the main reason that it cannot converge to stable solution.
RuntimeWarning: invalid value encountered in subtract df = fun(x) - f0
UserWarning: possible convergence problem: minimize gave code 2]

It does not seem as a big problem, though quite interesting that MA(1) from statsmodels does not get such error. I guess it successfully converges. As a result coefficient for MA(1) is different. And, what's more important, it calculates AIC criteria. This can can be important further for AutoARIMA.

from statsforecast.arima import arima
import numpy as np
import statsmodels.api as sm

np.random.seed(1234)

SIZE = 1_000
LOC = 1
SCALE = 1

eps = []

for i in range(SIZE):
    if i == 0:
        eps.append(np.random.normal(loc = LOC, scale = SCALE, size = 1)[0])
    elif (i > 0) & (i <= SIZE):
        eps.append(LOC + 0.99 * eps[i-1] + np.random.normal(loc = np.log(LOC), scale = np.sqrt(SCALE), size = 1)[0])

eps = np.array(eps)

ma_nixtla = arima(x = eps, order = (0, 0, 1))
ma_stats = sm.tsa.ARIMA(eps, order = (0, 0, 1)).fit()
ma_stats_ss  = sm.tsa.statespace.SARIMAX(eps, order = (0, 0, 1), trend='c').fit()

print(f'''
      MA coef are statsforecast:{ma_nixtla.get('coef').get('ma1')}, statsmodels tsa: {ma_stats.params[1]}, statsmodels statespace: {ma_stats_ss.params[1]}, \n
      Intercept coef are statsforecast:{ma_nixtla.get('coef').get('intercept')}, statsmodels tsa: {ma_stats.params[0]}, statsmodels statespace: {ma_stats_ss.params[0]}, \n
      AIC criteria are statsforecast:{ma_nixtla.get('aic')}, statsmodels tsa: {ma_stats.aic}, statsmodels statespace: {ma_stats_ss.aic}
      ''')

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I am also seeing a ton of RuntimeWarning: invalid value encountered in subtract at df = fun(x) - f0

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