While using ARMA to fit a model:
from statsmodels.tsa.arima_model import ARMA
I am getting a warning in my console:
C:\Users\lfc\anaconda3\lib\site-packages\statsmodels\tsa\arima_model.py:472: FutureWarning:
statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the . between arima and model) and statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.
statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and
is both well tested and maintained.
To silence this warning and continue using ARMA and ARIMA until they are
removed, use:
import warnings
warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',
FutureWarning)
warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',
FutureWarning)
warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)
How do I discard the warning?
Instead of using
from statsmodels.tsa.arima_model import ARIMA
Please change to following
from statsmodels.tsa.arima.model import ARIMA
Run the code below to ignore ARIMA warnings
import warnings
warnings.filterwarnings("ignore")
As of today, the statsmodels.tsa.arima_model.ARMA
and statsmodels.tsa.arima_model.ARIMA
have been removed in favor of statsmodels.tsa.arima.model.ARIMA
and statsmodels.tsa.SARIMAX
. This is because statsmodels.tsa.arima.model.ARIMA
makes use of the statespace framework and is both well tested and maintained. It also offers alternative specialized parameter estimators.
If you try to use ARMA
from statsmodels.tsa.arima_model
you'll get NotImplementedError
message error.
A quick fix to use ARIMA
model could be like this:
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(dataFrame.columnName, order=(1,0,0))
You can find more details in this issue .
This warning is occuring due to deprication of the ARIMA package "statsmodels\tsa\arima_model".
Instead, import the statsmodel with:
import statsmodels.api as sm
And fit ARIMA model as:
model = sm.tsa.arima.ARIMA(train_data, order=(1,1,2))
result = model.fit()
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