statsmodels

I am running a rolling for example of 100 window OLS regression estimation of the dataset found in this link ( https://drive.google.com/drive/folders/0B2Iv8dfU4fTUMVFyYTEtWXlzYkk ) as in the following format. time X Y 0.000543 0 10 0.000575 0 10 0.041324 1 10 0.041331 2 10 0.041336 3 10 0.04134 4 10 ... 9.987735 55 239 9.987739 56 239 9.987744 57 239 9.987749 58 239 9.987938 59 239 The third column (Y) in my dataset is my true value - that's what I wanted to predict (estimate). I want to do a prediction of Y (i.e. predict the current value of Y according to the previous 3 rolling values of X .

It is common to want to append the results of predictions to the dataset used to make the predictions, but the statsmodels predict function returns (non-indexed) results of a potentially different length than the dataset on which predictions are based. For example, if the test dataset, test , contains any null entries, then mod_fit = sm.Logit.from_formula('Y ~ A B C', train).fit() press = mod_fit.predict(test) will produce an array that is shorter than the length of test , and cannot be usefully appended with test['preds'] = preds And since the result of predict is not indexed, there is no way

I'm using statsmodels to fit a ARMA model. import statsmodels.api as sm arma = sm.tsa.ARMA(data, order =(4,4)); results = arma.fit( full_output=False, disp=0); Where data is a one-dimensional array. I know to get in-sample predictions: pred = results.predict(); Now, given a second data set data2 , how can I use the previously calibrated model to generate a series with forecasts (predictions) based in this observations? I thought there was an issue for this. If you file one on github, I'll be more likely to remember to add something like this. The prediction machinery is not (yet) available as

as the title suggests, where has the rolling function option in the ols command in Pandas migrated to in statsmodels? I can't seem to find it. Pandas tells me doom is in the works: FutureWarning: The pandas.stats.ols module is deprecated and will be removed in a future version. We refer to external packages like statsmodels, see some examples here: http://statsmodels.sourceforge.net/stable/regression.html model = pd.ols(y=series_1, x=mmmm, window=50) in fact, if you do something like: import statsmodels.api as sm model = sm.OLS(series_1, mmmm, window=50).fit() print(model.summary()) you get

I have a model set up in Python using the statsmodel glm function but now I want to add a sum to zero constraint to the model. The model is defined as follows: import statsmodels.formula.api as smf model = smf.glm(formula="A ~ B + C + D", data=data, family=sm.families.Poisson()).fit() In R, to add the constraint, I would simply do something like this: model <- glm(A ~ B + C + D –1, family=poisson(), data=data, contrasts=list(C="contr.sum", D="contr.sum")) That adds the sum to zero constraint to both C and D but I am not sure how to achieve the same in Python. I have seen that there is a fit

I have two numpy arrays light_points and time_points and would like to use some time series analysis methods on those data. I then tried this : import statsmodels.api as sm import pandas as pd tdf = pd.DataFrame({'time':time_points[:]}) rdf = pd.DataFrame({'light':light_points[:]}) rdf.index = pd.DatetimeIndex(freq='w',start=0,periods=len(rdf.light)) #rdf.index = pd.DatetimeIndex(tdf['time']) This works but is not doing the correct thing. Indeed, the measurements are not evenly time-spaced and if I just declare the time_points pandas DataFrame as the index of my frame, I get an error : rdf