:orphan:

.. _examples:

Examples
========

Below is a gallery of examples.


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    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will demonstrate the canonical correlation coefficient between the features X and the one-hot encoded target y has equivalent relationship with Fisher&#x27;s criterion in LDA (Linear Discriminant Analysis).">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_fisher_thumb.png
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  :doc:`/auto_examples/plot_fisher`

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      <div class="sphx-glr-thumbnail-title">Fisher's criterion in LDA</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we illustrate how to build a multi-output polynomial NARX model for time series prediction.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_narx_multi_thumb.png
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  :doc:`/auto_examples/plot_narx_multi`

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      <div class="sphx-glr-thumbnail-title">Multi-output NARX model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will demonstrate how to use make_narx to build (nonlinear) AutoRegressive (AR) models for time-series forecasting. The time series used is the monthly average atmospheric CO2 concentrations from 1958 and 2001. The objective is to forecast the CO2 concentration till nowadays with initial 18 months data.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_forecasting_thumb.png
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  :doc:`/auto_examples/plot_forecasting`

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      <div class="sphx-glr-thumbnail-title">Forecasting with (Nonlinear) AR models</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will compare the robustness of the three feature selection methods on affine transformed features.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_affinity_thumb.png
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  :doc:`/auto_examples/plot_affinity`

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      <div class="sphx-glr-thumbnail-title">Affine invariance</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we illustrate how to build a polynomial NARX model for time series prediction.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_narx_thumb.png
    :alt:

  :doc:`/auto_examples/plot_narx`

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      <div class="sphx-glr-thumbnail-title">Nonlinear AutoRegressive eXogenous (NARX) model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to prune dataset with minibatch based on FastCan. The method is compared to random data pruning.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_pruning_thumb.png
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  :doc:`/auto_examples/plot_pruning`

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      <div class="sphx-glr-thumbnail-title">Data pruning</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Let&#x27;s intuitively understand the two methods, h-correlation and eta-cosine, in FastCan.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_intuitive_thumb.png
    :alt:

  :doc:`/auto_examples/plot_intuitive`

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      <div class="sphx-glr-thumbnail-title">Intuitively explanation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will compare the computational speed of three different feature selection methods: h-correlation based FastCan, eta-cosine based FastCan, and baseline model based on sklearn.cross_decomposition.CCA.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_speed_thumb.png
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  :doc:`/auto_examples/plot_speed`

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      <div class="sphx-glr-thumbnail-title">Computational speed comparison</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this examples, we will compare the performance of feature selectors on the datasets, which contain redundant features. Here four types of features should be distinguished:">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_redundancy_thumb.png
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  :doc:`/auto_examples/plot_redundancy`

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      <div class="sphx-glr-thumbnail-title">Performance on redundant features</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare one-step-ahead NARX and multi-step-ahead NARX.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_narx_msa_thumb.png
    :alt:

  :doc:`/auto_examples/plot_narx_msa`

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      <div class="sphx-glr-thumbnail-title">Multi-step-ahead NARX model</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/plot_fisher
   /auto_examples/plot_narx_multi
   /auto_examples/plot_forecasting
   /auto_examples/plot_affinity
   /auto_examples/plot_narx
   /auto_examples/plot_pruning
   /auto_examples/plot_intuitive
   /auto_examples/plot_speed
   /auto_examples/plot_redundancy
   /auto_examples/plot_narx_msa


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  .. container:: sphx-glr-footer sphx-glr-footer-gallery

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

      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

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

      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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