/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/ |
H A D | test_mlemodel.py | 450 mod = MLEModel([1, 2], **kwargs) 481 mod = MLEModel(endog, **kwargs) 535 mod = MLEModel(endog, **kwargs) 577 mod = MLEModel(endog, **kwargs) 603 mod = MLEModel(endog, **kwargs) 631 mod = MLEModel([1], **kwargs) 635 mod = MLEModel([1.], **kwargs) 643 mod = MLEModel(['a'], **kwargs) 667 mod = MLEModel(endog, **kwargs) 883 mod = MLEModel( [all …]
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H A D | test_multivariate_switch_univariate.py | 40 mod = mlemodel.MLEModel(endog, k_states=1, k_posdef=1) 58 mod = mlemodel.MLEModel(endog, k_states=3, k_posdef=2) 84 if isinstance(mod, mlemodel.MLEModel): 143 if isinstance(mod, mlemodel.MLEModel):
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H A D | test_simulation_smoothing.py | 53 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs) 223 generated_model = mlemodel.MLEModel( 326 generated_model = mlemodel.MLEModel( 454 mod = mlemodel.MLEModel(obs, k_states=2, k_posdef=2, **kwargs) 502 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
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H A D | test_univariate.py | 23 from statsmodels.tsa.statespace.mlemodel import MLEModel 58 cls.mlemodel = MLEModel(data, k_states=k_states, **kwargs) 279 mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs) 540 mod = MLEModel(obs, k_states=3, k_posdef=3, **kwargs)
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H A D | test_models.py | 21 class Intercepts(mlemodel.MLEModel): 196 class LargeStateCovAR1(mlemodel.MLEModel):
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H A D | test_collapsed.py | 18 from statsmodels.tsa.statespace.mlemodel import MLEModel 49 cls.mlemodel = MLEModel(data, k_states=k_states, **kwargs) 440 mod = MLEModel(obs, k_states=2, k_posdef=2, **kwargs) 512 mod = MLEModel(obs, k_states=4, k_posdef=2, **kwargs)
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H A D | test_fixed_params.py | 22 mod = mlemodel.MLEModel([], 1) 36 mod = mlemodel.MLEModel([], 1) 136 mod = mlemodel.MLEModel([], 1)
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H A D | test_kalman.py | 31 from statsmodels.tsa.statespace.mlemodel import MLEModel 736 mod = MLEModel(endog, k_states=1, k_posdef=1) 770 mod = MLEModel(endog, k_states=2, k_posdef=2)
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H A D | test_smoothing.py | 396 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs) 582 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs) 765 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs) 936 mod = mlemodel.MLEModel(obs, k_states=3, k_posdef=3, **kwargs) 957 mod = mlemodel.MLEModel(obs, k_states=6, k_posdef=3, **kwargs)
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H A D | test_cfa_tvpvar.py | 63 class TVPVAR(mlemodel.MLEModel):
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
H A D | statespace_concentrated_scale.py | 79 class LocalLevel(sm.tsa.statespace.MLEModel): 152 class LocalLevelConcentrated(sm.tsa.statespace.MLEModel):
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H A D | statespace_custom_models.py | 106 class TVRegression(sm.tsa.statespace.MLEModel): 345 class TVRegressionExtended(sm.tsa.statespace.MLEModel): 641 class MultipleYsModel(sm.tsa.statespace.MLEModel):
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H A D | statespace_local_linear_trend.py | 127 class LocalLinearTrend(sm.tsa.statespace.MLEModel):
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H A D | statespace_tvpvar_mcmc_cfa.py | 376 class TVPVAR(sm.tsa.statespace.MLEModel):
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/ |
H A D | api.py | 5 from .mlemodel import MLEModel, MLEResults
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H A D | exponential_smoothing.py | 28 from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper 31 class ExponentialSmoothing(MLEModel): 559 @Appender(MLEModel.loglike.__doc__) 570 @Appender(MLEModel.filter.__doc__) 587 @Appender(MLEModel.smooth.__doc__)
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H A D | _quarterly_ar1.py | 19 class QuarterlyAR1(mlemodel.MLEModel):
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H A D | mlemodel.py | 86 class MLEModel(tsbase.TimeSeriesModel): class 138 super(MLEModel, self).__init__(endog=endog, exog=exog, 246 kwds = super(MLEModel, self)._get_init_kwds() 704 mlefit = super(MLEModel, self).fit(start_params, method=method, 928 MLEModel._loglike_param_names, MLEModel._loglike_param_defaults, 1358 _handle_args(MLEModel._score_param_names, 1359 MLEModel._score_param_defaults, *args, **kwargs)) 1490 _handle_args(MLEModel._hessian_param_names, 1491 MLEModel._hessian_param_defaults,
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H A D | varmax.py | 23 from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper 32 class VARMAX(MLEModel): 809 @Appender(MLEModel.simulate.__doc__)
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/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/ |
H A D | statespace.rst | 380 mlemodel.MLEModel 386 `SARIMAXResults` classes, which are built by extending `MLEModel` and 389 In simple cases, the model can be constructed entirely using the MLEModel 410 class AR2(sm.tsa.statespace.MLEModel): 549 - :py:meth:`fit <mlemodel.MLEModel.fit>` - estimate parameters via maximum 553 - :py:meth:`smooth <mlemodel.MLEModel.smooth>` - return a results object 561 - :py:meth:`param_names <mlemodel.MLEModel.param_names>` - names of the 563 - :py:meth:`state_names <mlemodel.MLEModel.state_names>` - names of the 571 - :py:meth:`filter <mlemodel.MLEModel.filter>` - return a results object 745 The :py:meth:`fit_constrained <mlemodel.MLEModel.fit_constrained>` method [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/docs/source/release/ |
H A D | version0.10.2.rst | 42 * :pr:`6050`: BUG: MLEModel now passes nobs to Representation
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/regression/ |
H A D | recursive_ls.py | 15 MLEModel, MLEResults, MLEResultsWrapper, PredictionResults, 30 class RecursiveLS(MLEModel): 136 return super(MLEModel, cls).from_formula(formula, data, subset,
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/ |
H A D | statespace_local_linear_trend.ipynb | 70 …n, we create a new class which extends from `statsmodels.tsa.statespace.MLEModel`. There are a num… 98 "class LocalLinearTrend(sm.tsa.statespace.MLEModel):\n", 179 …"Since we defined the local linear trend model as extending from `MLEModel`, the `fit()` method is…
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H A D | statespace_concentrated_scale.ipynb | 81 "class LocalLevel(sm.tsa.statespace.MLEModel):\n", 177 "class LocalLevelConcentrated(sm.tsa.statespace.MLEModel):\n",
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H A D | statespace_custom_models.ipynb | 9 …dels. This notebook shows various statespace models that subclass `sm.tsa.statespace.MLEModel`.\n", 108 "class TVRegression(sm.tsa.statespace.MLEModel):\n", 368 "class TVRegressionExtended(sm.tsa.statespace.MLEModel):\n", 666 "class MultipleYsModel(sm.tsa.statespace.MLEModel):\n",
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