U Kv fV @sNdZddlZddlmZmZddlmZddlm Z ddl m Z d d d Z dS)zX State space approach to estimating SARIMAX models. Author: Chad Fulton License: BSD-3 N) add_constantBunch)SARIMAX)SARIMAXSpecification) SARIMAXParamsrrrrrrrTFc  Cs|r|dkrt|nt|}t|||||||d} | j}| j}t| d} |dk rt| d} || _| jrv| j svt d| j r| j st dt ||| j| j| j| j | jd} | dkri} | dd| jfd|i| }|j| _t| |d }| |fS) a Estimate SARIMAX parameters using state space methods. Parameters ---------- endog : array_like Input time series array. order : tuple, optional The (p,d,q) order of the model for the number of AR parameters, differences, and MA parameters. Default is (0, 0, 0). seasonal_order : tuple, optional The (P,D,Q,s) order of the seasonal component of the model for the AR parameters, differences, MA parameters, and periodicity. Default is (0, 0, 0, 0). include_constant : bool, optional Whether to add a constant term in `exog` if it's not already there. The estimate of the constant will then appear as one of the `exog` parameters. If `exog` is None, then the constant will represent the mean of the process. enforce_stationarity : bool, optional Whether or not to transform the AR parameters to enforce stationarity in the autoregressive component of the model. Default is True. enforce_invertibility : bool, optional Whether or not to transform the MA parameters to enforce invertibility in the moving average component of the model. Default is True. concentrate_scale : bool, optional Whether or not to concentrate the scale (variance of the error term) out of the likelihood. This reduces the number of parameters estimated by maximum likelihood by one. start_params : array_like, optional Initial guess of the solution for the loglikelihood maximization. The AR polynomial must be stationary. If `enforce_invertibility=True` the MA poylnomial must be invertible. If not provided, default starting parameters are computed using the Hannan-Rissanen method. fit_kwargs : dict, optional Arguments to pass to the state space model's `fit` method. Returns ------- parameters : SARIMAXParams object other_results : Bunch Includes two components, `spec`, containing the `SARIMAXSpecification` instance corresponding to the input arguments; and `state_space_results`, corresponding to the results from the underlying state space model and Kalman filter / smoother. Notes ----- The primary reference is [1]_. References ---------- .. [1] Durbin, James, and Siem Jan Koopman. 2012. Time Series Analysis by State Space Methods: Second Edition. Oxford University Press. N)exogorderseasonal_orderenforce_stationarityenforce_invertibilityconcentrate_scale)specz]Given starting parameters imply a non-stationary AR process with `enforce_stationarity=True`.z^Given starting parameters imply a non-invertible MA process with `enforce_invertibility=True`.Zdispr start_params)rZstatespace_results)npZ ones_likerrendogr rparamsr Z is_stationary ValueErrorr Z is_invertiblerr r r setdefaultZfitr)rr r r Zinclude_constantr r rrZ fit_kwargsrpspmodZres_ssresrO/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/tsa/arima/estimators/statespace.py statespacesJ>      r) NrrTTTFNN) __doc__ZnumpyrZstatsmodels.tools.toolsrrZ"statsmodels.tsa.statespace.sarimaxrZ#statsmodels.tsa.arima.specificationrZstatsmodels.tsa.arima.paramsrrrrrrs