Add method for generating ARMA predictions for new data, unrelated to training data · Issue #2577 · statsmodels/statsmodels
It seems that the only way to use a trained ARMA model on new data is to use this internal function: _arma_predict_out_of_sample. Furthermore, it's not obvious how to do so. I would assume that for an ARMA(p,q) model, there are max(p.q) previous datapoints required to generate a new prediction. So If I had an AR(2,3) model I'd need to pass in 3 datapoints to get one new output sample. There is als
ARIMA - The computed initial AR coefficients are not stationary · Issue #1155 · statsmodels/statsmodels
I hate pepper this repo with "that's not what happens in R!" type issues, but I am converting an R prototype into python, and that is what I am up against. Given the following input set: 6.287,6.416,6.418,6.301,6.494,6.701,6.974,7.128,7.398,7.72,7.859,7.674,7.636,7.684,7.921,8.236,8.346,8.427,8.617,8.762,8.99,9.09,9.271,9.485,9.661,9.998,10.257,10.577,10.876,10.954,11.19,11.39,11.515 With order: 4
GitHub - statsmodels/statsmodels: Statsmodels: statistical modeling and econometrics in Python
Linear regression models: Ordinary least squares Generalized least squares Weighted least squares Least squares with autoregressive errors Quantile regression Recursive least squares Mixed Linear Model with mixed effects and variance components GLM: Generalized linear models with support for all of the one-parameter exponential family distributions Bayesian Mixed GLM for Binomial and Poisson GEE:
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statsmodels/statsmodels/api.py at main · statsmodels/statsmodels
statsmodels/examples at main · statsmodels/statsmodels
Examples
