.. _sphx_glr_auto_examples_model_selection:

.. _model_selection_examples:

Cross-validation examples
-------------------------

Examples of how to use the :mod:`pmdarima.model_selection` module to fit
timeseries models in a cross-validated fashion.

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   <br/>



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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="In addition to computing cross-validation scores, you can use cross-validation to produce predictions. Unlike traditional cross-validation, where folds are independent of one another, time-series folds may overlap (particularly in a sliding window). To account for this, folds that forecast the same time step average their forecasts using either a &quot;mean&quot; or &quot;median&quot; (tunable).">

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  .. image:: /auto_examples/model_selection/images/thumb/sphx_glr_example_cross_val_predict_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_model_selection_example_cross_val_predict.py`

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      <div class="sphx-glr-thumbnail-title">Cross-validation predictions</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip=" Like scikit-learn, pmdarima provides several different strategies for cross-validating your time series models. The interface was designed to behave as similarly as possible to that of scikit to make its usage as simple as possible.">

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  .. image:: /auto_examples/model_selection/images/thumb/sphx_glr_example_cross_validation_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_model_selection_example_cross_validation.py`

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      <div class="sphx-glr-thumbnail-title">Cross-validating your time series models</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/model_selection/example_cross_val_predict
   /auto_examples/model_selection/example_cross_validation