.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples\forecasting\plot_wls_search.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download__examples_forecasting_plot_wls_search.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr__examples_forecasting_plot_wls_search.py:


WLS - Search
-------------------------

.. GENERATED FROM PYTHON SOURCE LINES 6-140



.. image-sg:: /_examples/forecasting/images/sphx_glr_plot_wls_search_001.png
   :alt: plot wls search
   :srcset: /_examples/forecasting/images/sphx_glr_plot_wls_search_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    c:\users\kelda\desktop\repositories\virtualenvs\venv-py391-pyamr\lib\site-packages\statsmodels\regression\linear_model.py:807: RuntimeWarning:

    divide by zero encountered in log

    c:\users\kelda\desktop\repositories\virtualenvs\venv-py391-pyamr\lib\site-packages\statsmodels\regression\linear_model.py:807: RuntimeWarning:

    divide by zero encountered in log

    c:\users\kelda\desktop\repositories\virtualenvs\venv-py391-pyamr\lib\site-packages\statsmodels\regression\linear_model.py:807: RuntimeWarning:

    divide by zero encountered in log

    c:\users\kelda\desktop\repositories\virtualenvs\venv-py391-pyamr\lib\site-packages\statsmodels\regression\linear_model.py:807: RuntimeWarning:

    divide by zero encountered in log

    c:\users\kelda\desktop\repositories\virtualenvs\venv-py391-pyamr\lib\site-packages\statsmodels\regression\linear_model.py:807: RuntimeWarning:

    divide by zero encountered in log


    Grid search:
                                0              1              2              3              4              5
    wls-rsquared           0.7508         0.5715         0.5383         0.4427         0.4913         0.2871
    wls-rsquare...         0.7482         0.5671         0.5336          0.437         0.4861         0.2798
    wls-fvalue           295.1824       130.6808       114.2534        77.8336        94.6547         39.463
    wls-fprob                 0.0            0.0            0.0            0.0            0.0            0.0
    wls-aic             1127.8602            inf            inf            inf            inf            inf
    wls-bic             1133.0705            inf            inf            inf            inf            inf
    wls-llf             -561.9301           -inf           -inf           -inf           -inf           -inf
    wls-mse_model    1340457.1185     178972.628    137413.0249     76234.7768     74119.1589       21463.64
    wls-mse_resid       4541.1139      1369.5401       1202.704       979.4588       783.0477       543.8925
    wls-mse_total       18035.215      3163.5107      2578.5658      1739.6135      1523.8165        755.203
    wls-const_coef       169.0879       259.2213        274.591       314.9257       300.5514       381.1919
    wls-const_std         13.3771        16.5491        16.5363        15.9253         16.066        12.4215
    wls-const_t...        12.6401        15.6638        16.6053        19.7752        18.7073         30.688
    wls-const_t...            0.0            0.0            0.0            0.0            0.0            0.0
    wls-const_cil        142.5415       226.3802       241.7752       283.3224        268.669       356.5418
    wls-const_ciu        195.6343       292.0624       307.4068       346.5289       332.4338        405.842
    wls-x1_coef            4.0109         2.6599         2.4545         1.9319         2.1034         1.0746
    wls-x1_std             0.2334         0.2327         0.2296          0.219         0.2162         0.1711
    wls-x1_tvalue         17.1809        11.4316        10.6889         8.8223         9.7291          6.282
    wls-x1_tprob              0.0            0.0            0.0            0.0            0.0            0.0
    wls-x1_cil             3.5476         2.1981         1.9988         1.4973         1.6744         0.7352
    wls-x1_ciu             4.4741         3.1216         2.9102         2.3664         2.5325         1.4141
    wls-s_dw                 0.22          0.426          0.479          0.633          0.605          0.891
    wls-s_jb_value          4.002         11.477         12.933         17.487         13.908         20.172
    wls-s_jb_prob           0.135         0.0032         0.0015         0.0002          0.001            0.0
    wls-s_skew              0.489          0.532          0.544          0.654          0.596          0.418
    wls-s_kurtosis          2.935          4.274          4.386          4.577          4.384          5.035
    wls-s_omnib...          4.163          9.688         10.389          13.19         11.283         11.307
    wls-s_omnib...          0.125          0.008          0.006          0.001          0.004          0.004
    wls-m_dw               0.2198         0.1507         0.1349         0.0995         0.1111         0.0615
    wls-m_jb_value         4.0017         7.8715        10.8827        16.8792        15.2715        19.8386
    wls-m_jb_prob          0.1352         0.0195         0.0043         0.0002         0.0005            0.0
    wls-m_skew             0.4889        -0.6401        -0.7696        -0.9868         -0.932         -1.089
    wls-m_kurtosis         2.9346         3.5001         3.4926         3.3949         3.4364         3.1338
    wls-m_nm_value         4.1633         8.2523         10.723        14.9411        13.9033        16.4058
    wls-m_nm_prob          0.1247         0.0161         0.0047         0.0006          0.001         0.0003
    wls-m_ks_value           0.55           0.58         0.6195         0.6699         0.6092         0.6998
    wls-m_ks_prob             0.0            0.0            0.0            0.0            0.0            0.0
    wls-m_shp_v...         0.9703         0.9297         0.9143         0.8843         0.8922          0.862
    wls-m_shp_prob         0.0233            0.0            0.0            0.0            0.0            0.0
    wls-m_ad_value         1.0302         3.2026         3.9394         5.2416         4.9361         5.7562
    wls-m_ad_nnorm          False          False          False          False          False          False
    wls-exog        [[1.0, 0.0...  [[1.0, 0.0...  [[1.0, 0.0...  [[1.0, 0.0...  [[1.0, 0.0...  [[1.0, 0.0...
    wls-endog       [49.650303...  [49.650303...  [49.650303...  [49.650303...  [49.650303...  [49.650303...
    wls-trend                   c              c              c              c              c              c
    wls-weights     [1.0, 1.0,...  [0.0657166...  [0.0447889...  [0.0156449...  [0.0189971...  [1.9369381...
    wls-W           <statsmode...  <pyamr.met...  <pyamr.met...  <pyamr.met...  <pyamr.met...  <pyamr.met...
    wls-model       <statsmode...  <statsmode...  <statsmode...  <statsmode...  <statsmode...  <statsmode...
    wls-id          WLS(c,Leas...  WLS(c,Sig(...  WLS(c,Sig(...  WLS(c,Sig(...  WLS(c,Sig(...  WLS(c,Sig(...






|

.. code-block:: default
   :lineno-start: 6

    # Import class.
    import sys
    import numpy as np
    import pandas as pd
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import statsmodels.api as sm
    import statsmodels.robust.norms as norms

    # import weights.
    from pyamr.datasets.load import make_timeseries
    from pyamr.core.regression.wls import WLSWrapper
    from pyamr.metrics.weights import SigmoidA

    # ----------------------------
    # set basic configuration
    # ----------------------------
    # Matplotlib options
    mpl.rc('legend', fontsize=6)
    mpl.rc('xtick', labelsize=6)
    mpl.rc('ytick', labelsize=6)

    # Set pandas configuration.
    pd.set_option('display.max_colwidth', 14)
    pd.set_option('display.width', 150)
    pd.set_option('display.precision', 4)

    # ----------------------------
    # create data
    # ----------------------------
    # Create timeseries data
    x, y, f = make_timeseries()

    # -----------------------------
    # Example II
    # -----------------------------
    # This example performs grid search on a number of possible configurations
    # of the WLSWrapper. In particular, it tests the effect of different 
    # objects to compute the weights from the frequencies. It presents both
    # the resulting pandas dataframe and also a figure.

    # Configuration
    # -------------
    # This variable contains the weight functions to test. Note that in 
    # the norms module there are other options such as [norms.HuberT(), 
    # norms.Hampel(), norms.TrimmedMean(), norms.TukeyBiweight(), 
    # norms.AndreWave(), norms.RamsayE()]
    w_func = [
        norms.LeastSquares(),
        SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0),
        SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, percentiles=[10, 90]),
        SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, percentiles=[25, 75]),
        SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, percentiles=[25, 90]),
        SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, percentiles=[40, 50])]

    # The grid search parameters.
    grid_params = [
        # {'exog': [x], 'endog': [y], 'trend': ['c']},
        {'exog': [x], 'endog': [y], 'trend': ['c'], 'weights': [f], 'W': w_func}
    ]

    # Grid search
    # ------------
    # Perform grid search.
    summary = WLSWrapper(estimator=sm.WLS) \
        .grid_search(grid_params=grid_params)

    # Show grid results
    # ..todo: It is weird to create an WLSWrapper jut to
    #         be able to use themethod from_list_dataframe.
    #         try to implemented separately.
    print("\nGrid search:")
    print(WLSWrapper().from_list_dataframe(summary).T)

    # Prediction
    # ----------
    # Variables.
    start, end = 10, 150

    # Create figure
    fig, axes = plt.subplots(1, 3, figsize=(10, 5))

    # Plot truth values.
    axes[0].plot(x, y, color='#A6CEE3', alpha=0.5, marker='o',
                 markeredgecolor='k', markeredgewidth=0.5,
                 markersize=5, linewidth=0.75, label='Observed')

    # Plot frequencies
    axes[0].bar(x, f, color='gray', alpha=0.7, label='Frequency')

    # For each of the models in summary
    for i, model in enumerate(summary):

        # Compute predictions.
        preds = model.get_prediction(start=start, end=end)

        # Plot forecasted values.
        axes[0].plot(preds[0, :], preds[1, :],
                     linewidth=1.0,
                     label=model._identifier(short=True))

        # Plot the confidence intervals.
        axes[0].fill_between(preds[0, :],
                             preds[2, :],
                             preds[3, :],
                             alpha=0.1)

        # Plot weights assigned to each observation
        axes[1].plot(model.weights, marker='o', alpha=0.5,
                     markeredgecolor='k', markeredgewidth=0.5,
                     markersize=4, linewidth=0.00,
                     label=model._identifier(short=True))

        # Plot weights converter (W) functions.
        if model.W is not None:
            axes[2].plot(np.linspace(0, 1, 100),
                         model.W.weights(np.linspace(0, 1, 100)),
                         label=model._identifier(short=True))

    # Grid.
    axes[0].grid(linestyle='--', linewidth=0.35, alpha=0.5)
    axes[1].grid(linestyle='--', linewidth=0.35, alpha=0.5)
    axes[2].grid(linestyle='--', linewidth=0.35, alpha=0.5)

    # Legend.
    axes[0].legend(loc=0)
    axes[1].legend(loc=0)
    axes[2].legend(loc=0)

    # Tight layout
    plt.tight_layout()

    # Show.
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.539 seconds)


.. _sphx_glr_download__examples_forecasting_plot_wls_search.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_wls_search.py <plot_wls_search.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_wls_search.ipynb <plot_wls_search.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_