M/PhJdZddlZddlmZddlmZddlmZddl m Z d dZ dS) zZ Burg's method for estimating AR(p) model parameters. Author: Chad Fulton License: BSD-3 N)Bunch) linear_model)SARIMAXSpecification) SARIMAXParamsTct||}|j}tj|jtjt r|dz}|jstdt|}|dkrtj ||_ n$tj |||\|_ |_ td|i}||fS)a' Estimate AR parameters using Burg technique. Parameters ---------- endog : array_like or SARIMAXSpecification Input time series array, assumed to be stationary. ar_order : int, optional Autoregressive order. Default is 0. demean : bool, optional Whether to estimate and remove the mean from the process prior to fitting the autoregressive coefficients. Returns ------- parameters : SARIMAXParams object Contains the parameter estimates from the final iteration. other_results : Bunch Includes one component, `spec`, which is the `SARIMAXSpecification` instance corresponding to the input arguments. Notes ----- The primary reference is [1]_, section 5.1.2. This procedure assumes that the series is stationary. This function is a light wrapper around `statsmodels.linear_model.burg`. References ---------- .. [1] Brockwell, Peter J., and Richard A. Davis. 2016. Introduction to Time Series and Forecasting. Springer. )ar_orderg?z\Burg estimation unavailable for models with seasonal or otherwise non-consecutive AR orders.)specr)orderdemeanr )rendognp issubdtypedtypeintis_ar_consecutive ValueErrorrvarsigma2rburg ar_paramsr)r rr r p other_resultss e/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/tsa/arima/estimators/burg.pyrrsF  9 9 9D JE  }U["(3--00   !NMNN N 4   A1}}6%== , 1%x9?!A!A!A QXM m )rT) __doc__numpyr statsmodels.tools.toolsrstatsmodels.regressionr#statsmodels.tsa.arima.specificationrstatsmodels.tsa.arima.paramsrrrrr"s ))))))//////DDDDDD666666======r