4.8.1.2.1. statsmodels.tsa.api.AR¶
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class
statsmodels.tsa.api.AR(endog, dates=None, freq=None, missing='none')[source]¶ Autoregressive AR(p) model
Parameters: endog : array-like
1-d endogenous response variable. The independent variable.
dates : array-like of datetime, optional
An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex.
freq : str, optional
The frequency of the time-series. A Pandas offset or ‘B’, ‘D’, ‘W’, ‘M’, ‘A’, or ‘Q’. This is optional if dates are given.
missing : str
Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’
4.8.1.2.1.1. Methods¶
__init__(endog[, dates, freq, missing]) |
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fit([maxlag, method, ic, trend, ...]) |
Fit the unconditional maximum likelihood of an AR(p) process. |
from_formula(formula, data[, subset]) |
Create a Model from a formula and dataframe. |
hessian(params) |
Returns numerical hessian for now. |
information(params) |
Not Implemented Yet |
initialize() |
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loglike(params) |
The loglikelihood of an AR(p) process |
predict(params[, start, end, dynamic]) |
Returns in-sample and out-of-sample prediction. |
score(params) |
Return the gradient of the loglikelihood at params. |
select_order(maxlag, ic[, trend, method]) |
Select the lag order according to the information criterion. |
4.8.1.2.1.2. Attributes¶
endog_names |
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exog_names |