class_docstr: SARIMAX specification.
  
  Parameters
  ----------
  endog : array_like, optional
      The observed time-series process :math:`y`.
  exog : array_like, optional
      Array of exogenous regressors.
  order : tuple, optional
      The (p,d,q) order of the model for the autoregressive, differences, and
      moving average components. d is always an integer, while p and q may
      either be integers or lists of integers. May not be used in combination
      with the arguments `ar_order`, `diff`, or `ma_order`.
  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). D and s are always integers, while P and Q
      may either be integers or lists of positive integers. May not be used
      in combination with the arguments `seasonal_ar_order`, `seasonal_diff`,
      or `seasonal_ma_order`.
  ar_order : int or list of int
      The autoregressive order of the model. May be an integer, in which case
      all autoregressive lags up to and including it will be included.
      Alternatively, may be a list of integers specifying which lag orders
      are included. May not be used in combination with `order`.
  diff : int
      The order of integration of the model. May not be used in combination
      with `order`.
  ma_order : int or list of int
      The moving average order of the model. May be an integer or
      list of integers. See the documentation for `ar_order` for details.
      May not be used in combination with `order`.
  seasonal_ar_order : int or list of int
      The seasonal autoregressive order of the model. May be an integer or
      list of integers. See the documentation for `ar_order` for examples.
      Note that if `seasonal_periods = 4` and `seasonal_ar_order = 2`, then
      this implies that the overall model will include lags 4 and 8.
      May not be used in combination with `seasonal_order`.
  seasonal_diff : int
      The order of seasonal integration of the model. May not be used in
      combination with `seasonal_order`.
  seasonal_ma_order : int or list of int
      The moving average order of the model. May be an integer or
      list of integers. See the documentation for `ar_order` and
      `seasonal_ar_order` for additional details. May not be used in
      combination with `seasonal_order`.
  seasonal_periods : int
      Number of periods in a season. May not be used in combination with
      `seasonal_order`.
  enforce_stationarity : bool, optional
      Whether or not to require the autoregressive parameters to correspond
      to a stationarity process. This is only possible in estimation by
      numerical maximum likelihood.
  enforce_invertibility : bool, optional
      Whether or not to require the moving average parameters to correspond
      to an invertible process. This is only possible in estimation by
      numerical maximum likelihood.
  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 by one.
      This is only applicable when considering estimation by numerical
      maximum likelihood.
  dates : array_like of datetime, optional
      If no index is given by `endog` or `exog`, an array-like object of
      datetime objects can be provided.
  freq : str, optional
      If no index is given by `endog` or `exog`, the frequency of the
      time-series may be specified here as a Pandas offset or offset string.
  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'.
  
  Attributes
  ----------
  order : tuple, optional
      The (p,d,q) order of the model for the autoregressive, differences, and
      moving average components. d is always an integer, while p and q may
      either be integers or lists of integers.
  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). D and s are always integers, while P and Q
      may either be integers or lists of positive integers.
  ar_order : int or list of int
      The autoregressive order of the model. May be an integer, in which case
      all autoregressive lags up to and including it will be included. For
      example, if `ar_order = 3`, then the model will include lags 1, 2,
      and 3. Alternatively, may be a list of integers specifying exactly
      which lag orders are included. For example, if `ar_order = [1, 3]`,
      then the model will include lags 1 and 3 but will exclude lag 2.
  diff : int
      The order of integration of the model.
  ma_order : int or list of int
      The moving average order of the model. May be an integer or
      list of integers. See the documentation for `ar_order` for examples.
  seasonal_ar_order : int or list of int
      The seasonal autoregressive order of the model. May be an integer or
      list of integers. See the documentation for `ar_order` for examples.
      Note that if `seasonal_periods = 4` and `seasonal_ar_order = 2`, then
      this implies that the overall model will include lags 4 and 8.
  seasonal_diff : int
      The order of seasonal integration of the model.
  seasonal_ma_order : int or list of int
      The moving average order of the model. May be an integer or
      list of integers. See the documentation for `ar_order` and
      `seasonal_ar_order` for additional details.
  seasonal_periods : int
      Number of periods in a season.
  trend : str{'n','c','t','ct'} or iterable, optional
      Parameter controlling the deterministic trend polynomial :math:`A(t)`.
      Can be specified as a string where 'c' indicates a constant (i.e. a
      degree zero component of the trend polynomial), 't' indicates a
      linear trend with time, and 'ct' is both. Can also be specified as an
      iterable defining the polynomial as in `numpy.poly1d`, where
      `[1,1,0,1]` would denote :math:`a + bt + ct^3`. Default is to not
      include a trend component.
  ar_lags : list of int
      List of included autoregressive lags. If `ar_order` is a list, then
      `ar_lags == ar_order`. If `ar_lags = [1, 2]`, then the overall model
      will include the 1st and 2nd autoregressive lags.
  ma_lags : list of int
      List of included moving average lags. If `ma_order` is a list, then
      `ma_lags == ma_order`. If `ma_lags = [1, 2]`, then the overall model
      will include the 1st and 2nd moving average lags.
  seasonal_ar_lags : list of int
      List of included seasonal autoregressive lags. If `seasonal_ar_order`
      is a list, then `seasonal_ar_lags == seasonal_ar_order`. If
      `seasonal_periods = 4` and `seasonal_ar_lags = [1, 2]`, then the
      overall model will include the 4th and 8th autoregressive lags.
  seasonal_ma_lags : list of int
      List of included seasonal moving average lags. If `seasonal_ma_order`
      is a list, then `seasonal_ma_lags == seasonal_ma_order`. See the
      documentation to `seasonal_ar_lags` for examples.
  max_ar_order : int
      Largest included autoregressive lag.
  max_ma_order : int
      Largest included moving average lag.
  max_seasonal_ar_order : int
      Largest included seasonal autoregressive lag.
  max_seasonal_ma_order : int
      Largest included seasonal moving average lag.
  max_reduced_ar_order : int
      Largest lag in the reduced autoregressive polynomial. Equal to
      `max_ar_order + max_seasonal_ar_order * seasonal_periods`.
  max_reduced_ma_order : int
      Largest lag in the reduced moving average polynomial. Equal to
      `max_ma_order + max_seasonal_ma_order * seasonal_periods`.
  enforce_stationarity : bool
      Whether or not to transform the AR parameters to enforce stationarity
      in the autoregressive component of the model. This is only possible
      in estimation by numerical maximum likelihood.
  enforce_invertibility : bool
      Whether or not to transform the MA parameters to enforce invertibility
      in the moving average component of the model. This is only possible
      in estimation by numerical maximum likelihood.
  concentrate_scale : bool
      Whether or not to concentrate the variance (scale term) out of the
      log-likelihood function. This is only applicable when considering
      estimation by numerical maximum likelihood.
  is_ar_consecutive
  is_ma_consecutive
  is_integrated
  is_seasonal
  k_exog_params
  k_ar_params
  k_ma_params
  k_seasonal_ar_params
  k_seasonal_ma_params
  k_params
  exog_names
  ar_names
  ma_names
  seasonal_ar_names
  seasonal_ma_names
  param_names
  
  Examples
  --------
  >>> SARIMAXSpecification(order=(1, 0, 2))
  SARIMAXSpecification(endog=y, order=(1, 0, 2))
  
  >>> spec = SARIMAXSpecification(ar_order=1, ma_order=2)
  SARIMAXSpecification(endog=y, order=(1, 0, 2))
  
  >>> spec = SARIMAXSpecification(ar_order=1, seasonal_order=(1, 0, 0, 4))
  SARIMAXSpecification(endog=y, order=(1, 0, 0), seasonal_order=(1, 0, 0, 4))
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docstr: (bool) Does the model include a seasonal component.
docstr_tok: ["(","bool",")","Does","the","model","include","a","seasonal","component","."]
code: def is_seasonal(self):
      """(bool) Does the model include a seasonal component."""
      return self.seasonal_periods != 0
  
imports: import numpy
  import pandas
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.arima.tools import standardize_lag_order, validate_basic
  from statsmodels.tsa.base.tsa_model import TimeSeriesModel
  from statsmodels.tsa.statespace.tools import constrain_stationary_univariate, is_invertible, prepare_exog, prepare_trend_data, prepare_trend_spec, unconstrain_stationary_univariate
label_desc: Use image node_id 5 for calling the SARIMAXSpecification obj's underlying member method code with example usage: obj.is_seasonal() and returns: unknown