I’m trying to build old school model using only auto regression algorithm. I found out that there’s an implementation of it in statsmodel package. I’ve read the documentation, and as I understand it should work as ARIMA. So, here’s my code: And when I want to predict new values, I’m trying to follow the documentation: Both returns a list of
Tag: statsmodels
Missing observations and clustered standard errors in Python statsmodels?
What’s the cleanest, most pythonic way to run a regression only on non-missing data and use clustered standard errors? Imagine I have a Pandas dataframe all_data. Clunky method that works (make a dataframe without missing data): I can make a new dataframe without the missing data, make the model, and fit the model: This feels a bit clunky (esp. when
How to predict new values using statsmodels.formula.api (python)
I trained the logistic model using the following, from breast cancer data and ONLY using one feature ‘mean_area’ There is a built in predict method in the trained model. However that gives the predicted values of all the training samples. As follows Suppose I want the prediction for a new value say 30 How do I used the trained model
statsmodels logistic regression odds ratio
I’m wondering how can I get odds ratio from a fitted logistic regression models in python statsmodels. Answer You can get the odds ratio with: To also get the confidence intervals (source): Disclaimer: I’ve just put together the comments to your question.
Print ‘std err’ value from statsmodels OLS results
(Sorry to ask but http://statsmodels.sourceforge.net/ is currently down and I can’t access the docs) I’m doing a linear regression using statsmodels, basically: I know that I can print out the full set of results with: which outputs something like: I need a way to print out only the values of coef and std err. I can access coef with: but
auto.arima() equivalent for python
I am trying to predict weekly sales using ARMA ARIMA models. I could not find a function for tuning the order(p,d,q) in statsmodels. Currently R has a function forecast::auto.arima() which will tune the (p,d,q) parameters. How do I go about choosing the right order for my model? Are there any libraries available in python for this purpose? Answer You can
Weighted standard deviation in NumPy
numpy.average() has a weights option, but numpy.std() does not. Does anyone have suggestions for a workaround? Answer How about the following short “manual calculation”?