Join GitHub today
GitHub is home to over 36 million developers working together to host and review code, manage projects, and build software together.Sign up
I don't believe that the confint parameter is being implemented in http://statsmodels.sourceforge.net/devel/generated/statsmodels.tsa.ar_model.ARResults.predict.html
ARResults.predict(start=None, end=None, dynamic=False)
I think it is still not implemented. It has been closed though. Any explanation ? Or Am I doing something wrong (I have an instance of
Thank you for your help
@josef-pkt Sorry to come back to you. I tried what you suggested and I just can't fit my model with SARIMAX (I also tried with ARMA and ARIMA), even though it is perfectly fine with AR (and I get pretty good predictions on my testing data)
Here what I have
fit_AR = AR(training_data).fit(MAX_LAG_MODELING).params.values print(fit_AR) fit_SARIMAX = SARIMAX(training_data, order = (1, 0, MAX_LAG_MODELING)).fit(disp = False) print(fit_SARIMAX)
The second line prints the weights correctly (and they make sense when you compare them with the data).
For the third line I get
I don't see how with only one AM parameter I should get a non invertible filter (maybe if the weights becomes zero but then there might be an issue in the solver (I tried others solvers without any luck).
I tried tweeking the params and sometimes I get a different error. It says that the data is not stationary. Which is false according to the adf test.
I can't lower my
I hope there is a way to get these confidence intervals. Thank you very much for your help
You specified a MA model not an AR, ARMA(ar_order, differencing, ma_order)
Given that you have seasonal data, you could try the seasonal ARMA specification, which would require fewer parameters than a full length AR. See docstring and example notebooks for SARIMAX
I'm sorry, but it's still not working. It is taking 100% of my CPU and never find a solution. With the AR model it takes less than a second. I don't understand this difference since I only have AR parameters. Is it normal ?
EDIT: It never finished actually (SIGKILL from the OS because it was taking too much ram)