# Issues making series stationary

I am trying to run some ARIMA forecasts and I switched recently from R to Python.

I am struggling for some reason to make this series stationary .

I try to take the log returns of stock prices as such :

$$ret = \ln{\frac{P_{i}}{P_{i-1}}}$$

I am using this line in Python:

[x for x in np.log(df.price/df.price.shift(-1)) if str(x) != 'nan']


see the first observations in the data

          date         price    logret  fcast
0    2020-08-03  11823.690000  0.041427    0.0
1    2020-08-02  11343.880000  0.020389    0.0
2    2020-08-01  11114.930000  0.001104    0.0
3    2020-07-31  11102.670000  0.015222    0.0


the adFuller test returns :

adfuller(df.logret)
(-17.0273775639977, 8.372792842473242e-30, 1, 578, {'1%': -3.441714324024304, '5%': -2.8665533998436215, '10%': -2.5694399997605393}, -1971.579460294467)


from my understanding this series should be stationary.

But when I run a for loop to generate the ARIMA grid I get a weird error telling me that series is not stationary.

AR/MA(5,5)

ERROR Example:
HessianInversionWarning: Inverting hessian failed, no bse or cov_params available
warn('Inverting hessian failed, no bse or cov_params '


AR/MA(10,10)

Another ERROR :

ValueError: The computed initial MA coefficients are not invertible
You should induce invertibility, choose a different model order, or you can
pass your own start_params.

• it's hard to know what the problem might be without being able to re-run your code ourselves - can you add a snippet that reproduces the above error? Right now, it could be a data problem, a data wrangling problem, a code problem, or a numerical problem... – StackG Aug 3 at 0:49

df.price = pd.to_numeric(df.price)