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<section id="pyfts-data-package">
<h1>pyFTS.data package<a class="headerlink" href="#pyfts-data-package" title="Permalink to this headline"></a></h1>
<div class="toctree-wrapper compound">
</div>
<section id="module-pyFTS.data">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.data" title="Permalink to this headline"></a></h2>
<p>Module for pyFTS standard datasets facilities</p>
</section>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</section>
<section id="module-pyFTS.data.common">
<span id="pyfts-data-common-module"></span><h2>pyFTS.data.common module<a class="headerlink" href="#module-pyFTS.data.common" title="Permalink to this headline"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.common.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.common.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">url</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sep</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">';'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'infer'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/common.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.common.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>This method check if filename already exists, read the file and return its data.
If the file don’t already exists, it will be downloaded and decompressed.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>filename</strong> – dataset local filename</p></li>
<li><p><strong>url</strong> – dataset internet URL</p></li>
<li><p><strong>sep</strong> – CSV field separator</p></li>
<li><p><strong>compression</strong> – type of compression</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Pandas dataset</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="datasets">
<h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline"></a></h2>
</section>
<section id="module-pyFTS.data.artificial">
<span id="artificial-and-synthetic-data-generators"></span><h2>Artificial and synthetic data generators<a class="headerlink" href="#module-pyFTS.data.artificial" title="Permalink to this headline"></a></h2>
<p>Facilities to generate synthetic stochastic processes</p>
<dl class="py class">
<dt class="sig sig-object py" id="pyFTS.data.artificial.SignalEmulator">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.data.artificial.</span></span><span class="sig-name descname"><span class="pre">SignalEmulator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
<p>Emulate a complex signal built from several additive and non-additive components</p>
<dl class="py method">
<dt class="sig sig-object py" id="pyFTS.data.artificial.SignalEmulator.blip">
<span class="sig-name descname"><span class="pre">blip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.blip"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.blip" title="Permalink to this definition"></a></dt>
<dd><p>Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,
and insert it on a random location of the signal.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the current SignalEmulator instance, for method chaining</p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="pyFTS.data.artificial.SignalEmulator.components">
<span class="sig-name descname"><span class="pre">components</span></span><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.components" title="Permalink to this definition"></a></dt>
<dd><p>Components of the signal</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="pyFTS.data.artificial.SignalEmulator.incremental_gaussian">
<span class="sig-name descname"><span class="pre">incremental_gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.incremental_gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.incremental_gaussian" title="Permalink to this definition"></a></dt>
<dd><p>Creates an additive gaussian interference on a previous signal</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mu</strong> – increment on mean</p></li>
<li><p><strong>sigma</strong> – increment on variance</p></li>
<li><p><strong>start</strong> – lag index to start this signal, the default value is 0</p></li>
<li><p><strong>it</strong> – Number of iterations, the default value is 1</p></li>
<li><p><strong>length</strong> – Number of samples generated on each iteration, the default value is 100</p></li>
<li><p><strong>vmin</strong> – Lower bound value of generated data, the default value is None</p></li>
<li><p><strong>vmax</strong> – Upper bound value of generated data, the default value is None</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the current SignalEmulator instance, for method chaining</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="pyFTS.data.artificial.SignalEmulator.periodic_gaussian">
<span class="sig-name descname"><span class="pre">periodic_gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">period</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu_min</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_min</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu_max</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_max</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.periodic_gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.periodic_gaussian" title="Permalink to this definition"></a></dt>
<dd><p>Creates an additive periodic gaussian interference on a previous signal</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>type</strong> – ‘linear’ or ‘sinoidal’</p></li>
<li><p><strong>period</strong> – the period of recurrence</p></li>
<li><p><strong>mu</strong> – increment on mean</p></li>
<li><p><strong>sigma</strong> – increment on variance</p></li>
<li><p><strong>start</strong> – lag index to start this signal, the default value is 0</p></li>
<li><p><strong>it</strong> – Number of iterations, the default value is 1</p></li>
<li><p><strong>length</strong> – Number of samples generated on each iteration, the default value is 100</p></li>
<li><p><strong>vmin</strong> – Lower bound value of generated data, the default value is None</p></li>
<li><p><strong>vmax</strong> – Upper bound value of generated data, the default value is None</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the current SignalEmulator instance, for method chaining</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="pyFTS.data.artificial.SignalEmulator.run">
<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.run" title="Permalink to this definition"></a></dt>
<dd><p>Render the signal</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>a list of float values</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="pyFTS.data.artificial.SignalEmulator.stationary_gaussian">
<span class="sig-name descname"><span class="pre">stationary_gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.stationary_gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.stationary_gaussian" title="Permalink to this definition"></a></dt>
<dd><p>Creates a continuous Gaussian signal with mean mu and variance sigma.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mu</strong> – mean</p></li>
<li><p><strong>sigma</strong> – variance</p></li>
<li><p><strong>additive</strong> – If False it cancels the previous signal and start this one, if True
this signal is added to the previous one</p></li>
<li><p><strong>start</strong> – lag index to start this signal, the default value is 0</p></li>
<li><p><strong>it</strong> – Number of iterations, the default value is 1</p></li>
<li><p><strong>length</strong> – Number of samples generated on each iteration, the default value is 100</p></li>
<li><p><strong>vmin</strong> – Lower bound value of generated data, the default value is None</p></li>
<li><p><strong>vmax</strong> – Upper bound value of generated data, the default value is None</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the current SignalEmulator instance, for method chaining</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.artificial.generate_gaussian_linear">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.artificial.</span></span><span class="sig-name descname"><span class="pre">generate_gaussian_linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mu_ini</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_ini</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu_inc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_inc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">it</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_gaussian_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_gaussian_linear" title="Permalink to this definition"></a></dt>
<dd><p>Generate data sampled from Gaussian distribution, with constant or linear changing parameters</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mu_ini</strong> – Initial mean</p></li>
<li><p><strong>sigma_ini</strong> – Initial variance</p></li>
<li><p><strong>mu_inc</strong> – Mean increment after ‘num’ samples</p></li>
<li><p><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</p></li>
<li><p><strong>it</strong> – Number of iterations</p></li>
<li><p><strong>num</strong> – Number of samples generated on each iteration</p></li>
<li><p><strong>vmin</strong> – Lower bound value of generated data</p></li>
<li><p><strong>vmax</strong> – Upper bound value of generated data</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of it*num float values</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.artificial.generate_linear_periodic_gaussian">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.artificial.</span></span><span class="sig-name descname"><span class="pre">generate_linear_periodic_gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">period</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu_min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">it</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_linear_periodic_gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_linear_periodic_gaussian" title="Permalink to this definition"></a></dt>
<dd><p>Generates a periodic linear variation on mean and variance</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>period</strong> – the period of recurrence</p></li>
<li><p><strong>mu_min</strong> – initial (and minimum) mean of each period</p></li>
<li><p><strong>sigma_min</strong> – initial (and minimum) variance of each period</p></li>
<li><p><strong>mu_max</strong> – final (and maximum) mean of each period</p></li>
<li><p><strong>sigma_max</strong> – final (and maximum) variance of each period</p></li>
<li><p><strong>it</strong> – Number of iterations</p></li>
<li><p><strong>num</strong> – Number of samples generated on each iteration</p></li>
<li><p><strong>vmin</strong> – Lower bound value of generated data</p></li>
<li><p><strong>vmax</strong> – Upper bound value of generated data</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of it*num float values</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.artificial.generate_sinoidal_periodic_gaussian">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.artificial.</span></span><span class="sig-name descname"><span class="pre">generate_sinoidal_periodic_gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">period</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu_min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mu_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">it</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_sinoidal_periodic_gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_sinoidal_periodic_gaussian" title="Permalink to this definition"></a></dt>
<dd><p>Generates a periodic sinoidal variation on mean and variance</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>period</strong> – the period of recurrence</p></li>
<li><p><strong>mu_min</strong> – initial (and minimum) mean of each period</p></li>
<li><p><strong>sigma_min</strong> – initial (and minimum) variance of each period</p></li>
<li><p><strong>mu_max</strong> – final (and maximum) mean of each period</p></li>
<li><p><strong>sigma_max</strong> – final (and maximum) variance of each period</p></li>
<li><p><strong>it</strong> – Number of iterations</p></li>
<li><p><strong>num</strong> – Number of samples generated on each iteration</p></li>
<li><p><strong>vmin</strong> – Lower bound value of generated data</p></li>
<li><p><strong>vmax</strong> – Upper bound value of generated data</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of it*num float values</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.artificial.generate_uniform_linear">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.artificial.</span></span><span class="sig-name descname"><span class="pre">generate_uniform_linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_ini</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_ini</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_inc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_inc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">it</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_uniform_linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_uniform_linear" title="Permalink to this definition"></a></dt>
<dd><p>Generate data sampled from Uniform distribution, with constant or linear changing bounds</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mu_ini</strong> – Initial mean</p></li>
<li><p><strong>sigma_ini</strong> – Initial variance</p></li>
<li><p><strong>mu_inc</strong> – Mean increment after ‘num’ samples</p></li>
<li><p><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</p></li>
<li><p><strong>it</strong> – Number of iterations</p></li>
<li><p><strong>num</strong> – Number of samples generated on each iteration</p></li>
<li><p><strong>vmin</strong> – Lower bound value of generated data</p></li>
<li><p><strong>vmax</strong> – Upper bound value of generated data</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of it*num float values</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.artificial.random_walk">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.artificial.</span></span><span class="sig-name descname"><span class="pre">random_walk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#random_walk"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.random_walk" title="Permalink to this definition"></a></dt>
<dd><p>Simple random walk</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>n</strong> – number of samples</p></li>
<li><p><strong>type</strong> – ‘gaussian’ or ‘uniform’</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.artificial.white_noise">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.artificial.</span></span><span class="sig-name descname"><span class="pre">white_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#white_noise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.artificial.white_noise" title="Permalink to this definition"></a></dt>
<dd><p>Simple Gaussian noise signal
:param n: number of samples
:return:</p>
</dd></dl>
</section>
<section id="module-pyFTS.data.AirPassengers">
<span id="airpassengers-dataset"></span><h2>AirPassengers dataset<a class="headerlink" href="#module-pyFTS.data.AirPassengers" title="Permalink to this headline"></a></h2>
<p>Monthly totals of a airline passengers from USA, from January 1949 through December 1960.</p>
<p>Source: Hyndman, R.J., Time Series Data Library, <a class="reference external" href="http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/">http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/</a>.</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.AirPassengers.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.AirPassengers.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/AirPassengers.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.AirPassengers.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.AirPassengers.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.AirPassengers.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/AirPassengers.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.AirPassengers.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.Bitcoin">
<span id="bitcoin-dataset"></span><h2>Bitcoin dataset<a class="headerlink" href="#module-pyFTS.data.Bitcoin" title="Permalink to this headline"></a></h2>
<p>Bitcoin to USD quotations</p>
<p>Daily averaged index, by business day, from 2010 to 2018.</p>
<p>Source: <a class="reference external" href="https://finance.yahoo.com/quote/BTC-USD?p=BTC-USD">https://finance.yahoo.com/quote/BTC-USD?p=BTC-USD</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Bitcoin.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Bitcoin.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'AVG'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Bitcoin.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Bitcoin.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – dataset field to load</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Bitcoin.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Bitcoin.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Bitcoin.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Bitcoin.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.DowJones">
<span id="dowjones-dataset"></span><h2>DowJones dataset<a class="headerlink" href="#module-pyFTS.data.DowJones" title="Permalink to this headline"></a></h2>
<p>DJI - Dow Jones</p>
<p>Daily averaged index, by business day, from 1985 to 2017.</p>
<p>Source: <a class="reference external" href="https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC">https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.DowJones.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.DowJones.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'AVG'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/DowJones.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.DowJones.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – dataset field to load</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.DowJones.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.DowJones.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/DowJones.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.DowJones.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.Enrollments">
<span id="enrollments-dataset"></span><h2>Enrollments dataset<a class="headerlink" href="#module-pyFTS.data.Enrollments" title="Permalink to this headline"></a></h2>
<p>Yearly University of Alabama enrollments from 1971 to 1992.</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Enrollments.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Enrollments.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Enrollments.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Enrollments.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Enrollments.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Enrollments.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Enrollments.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Enrollments.get_dataframe" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</section>
<section id="module-pyFTS.data.Ethereum">
<span id="ethereum-dataset"></span><h2>Ethereum dataset<a class="headerlink" href="#module-pyFTS.data.Ethereum" title="Permalink to this headline"></a></h2>
<p>Ethereum to USD quotations</p>
<p>Daily averaged index, by business day, from 2016 to 2018.</p>
<p>Source: <a class="reference external" href="https://finance.yahoo.com/quote/ETH-USD?p=ETH-USD">https://finance.yahoo.com/quote/ETH-USD?p=ETH-USD</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Ethereum.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Ethereum.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'AVG'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Ethereum.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Ethereum.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – dataset field to load</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Ethereum.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Ethereum.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Ethereum.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Ethereum.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.EURGBP">
<span id="eur-gbp-dataset"></span><h2>EUR-GBP dataset<a class="headerlink" href="#module-pyFTS.data.EURGBP" title="Permalink to this headline"></a></h2>
<p>FOREX market EUR-GBP pair.</p>
<p>Daily averaged quotations, by business day, from 2016 to 2018.</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.EURGBP.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.EURGBP.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'avg'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/EURGBP.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.EURGBP.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – dataset field to load</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.EURGBP.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.EURGBP.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/EURGBP.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.EURGBP.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.EURUSD">
<span id="eur-usd-dataset"></span><h2>EUR-USD dataset<a class="headerlink" href="#module-pyFTS.data.EURUSD" title="Permalink to this headline"></a></h2>
<p>FOREX market EUR-USD pair.</p>
<p>Daily averaged quotations, by business day, from 2016 to 2018.</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.EURUSD.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.EURUSD.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'avg'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/EURUSD.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.EURUSD.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – dataset field to load</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.EURUSD.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.EURUSD.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/EURUSD.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.EURUSD.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.GBPUSD">
<span id="gbp-usd-dataset"></span><h2>GBP-USD dataset<a class="headerlink" href="#module-pyFTS.data.GBPUSD" title="Permalink to this headline"></a></h2>
<p>FOREX market GBP-USD pair.</p>
<p>Daily averaged quotations, by business day, from 2016 to 2018.</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.GBPUSD.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.GBPUSD.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'avg'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/GBPUSD.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.GBPUSD.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – dataset field to load</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.GBPUSD.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.GBPUSD.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/GBPUSD.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.GBPUSD.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.INMET">
<span id="inmet-dataset"></span><h2>INMET dataset<a class="headerlink" href="#module-pyFTS.data.INMET" title="Permalink to this headline"></a></h2>
<p>INMET - Instituto Nacional Meteorologia / Brasil</p>
<p>Belo Horizonte station, from 2000-01-01 to 31/12/2012</p>
<p>Source: <a class="reference external" href="http://www.inmet.gov.br">http://www.inmet.gov.br</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.INMET.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.INMET.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/INMET.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.INMET.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.Malaysia">
<span id="malaysia-dataset"></span><h2>Malaysia dataset<a class="headerlink" href="#module-pyFTS.data.Malaysia" title="Permalink to this headline"></a></h2>
<p>Hourly Malaysia eletric load and tempeature</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Malaysia.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Malaysia.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'load'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Malaysia.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Malaysia.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – dataset field to load</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.Malaysia.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.Malaysia.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/Malaysia.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.Malaysia.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.NASDAQ">
<span id="nasdaq-module"></span><h2>NASDAQ module<a class="headerlink" href="#module-pyFTS.data.NASDAQ" title="Permalink to this headline"></a></h2>
<p>National Association of Securities Dealers Automated Quotations - Composite Index (NASDAQ IXIC)</p>
<p>Daily averaged index by business day, from 2000 to 2016.</p>
<p>Source: <a class="reference external" href="http://www.nasdaq.com/aspx/flashquotes.aspx?symbol=IXIC&amp;selected=IXIC">http://www.nasdaq.com/aspx/flashquotes.aspx?symbol=IXIC&amp;selected=IXIC</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.NASDAQ.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.NASDAQ.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'avg'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/NASDAQ.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.NASDAQ.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – the dataset field name to extract</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.NASDAQ.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.NASDAQ.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/NASDAQ.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.NASDAQ.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.SONDA">
<span id="sonda-dataset"></span><h2>SONDA dataset<a class="headerlink" href="#module-pyFTS.data.SONDA" title="Permalink to this headline"></a></h2>
<p>SONDA - Sistema de Organização Nacional de Dados Ambientais, from INPE - Instituto Nacional de Pesquisas Espaciais, Brasil.</p>
<p>Brasilia station</p>
<p>Source: <a class="reference external" href="http://sonda.ccst.inpe.br/">http://sonda.ccst.inpe.br/</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.SONDA.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.SONDA.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/SONDA.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.SONDA.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>field</strong> – the dataset field name to extract</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.SONDA.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.SONDA.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/SONDA.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.SONDA.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.SP500">
<span id="s-p-500-dataset"></span><h2>S&amp;P 500 dataset<a class="headerlink" href="#module-pyFTS.data.SP500" title="Permalink to this headline"></a></h2>
<p>S&amp;P500 - Standard &amp; Poor’s 500</p>
<p>Daily averaged index, by business day, from 1950 to 2017.</p>
<p>Source: <a class="reference external" href="https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC">https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.SP500.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.SP500.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/SP500.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.SP500.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.SP500.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.SP500.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/SP500.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.SP500.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.TAIEX">
<span id="taiex-dataset"></span><h2>TAIEX dataset<a class="headerlink" href="#module-pyFTS.data.TAIEX" title="Permalink to this headline"></a></h2>
<p>The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX)</p>
<p>Daily averaged index by business day, from 1995 to 2014.</p>
<p>Source: <a class="reference external" href="http://www.twse.com.tw/en/products/indices/Index_Series.php">http://www.twse.com.tw/en/products/indices/Index_Series.php</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.TAIEX.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.TAIEX.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/TAIEX.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.TAIEX.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get the univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.TAIEX.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.TAIEX.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/TAIEX.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.TAIEX.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.henon">
<span id="henon-chaotic-time-series"></span><h2>Henon chaotic time series<a class="headerlink" href="#module-pyFTS.data.henon" title="Permalink to this headline"></a></h2>
<ol class="upperalpha simple" start="13">
<li><p>Hénon. “A two-dimensional mapping with a strange attractor”. Commun. Math. Phys. 50, 69-77 (1976)</p></li>
</ol>
<p>dx/dt = a + by(t-1) - x(t-1)^2
dy/dt = x</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.henon.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.henon.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[1,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/henon.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.henon.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>var</strong> – the dataset field name to extract</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.henon.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.henon.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[1,</span> <span class="pre">1]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/henon.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.henon.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Return a dataframe with the bivariate Henon Map time series (x, y).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>a</strong> – Equation coefficient</p></li>
<li><p><strong>b</strong> – Equation coefficient</p></li>
<li><p><strong>initial_values</strong> – numpy array with the initial values of x and y. Default: [1, 1]</p></li>
<li><p><strong>iterations</strong> – number of iterations. Default: 1000</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Panda dataframe with the x and y values</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.logistic_map">
<span id="logistic-map-chaotic-time-series"></span><h2>Logistic_map chaotic time series<a class="headerlink" href="#module-pyFTS.data.logistic_map" title="Permalink to this headline"></a></h2>
<p>May, Robert M. (1976). “Simple mathematical models with very complicated dynamics”.
Nature. 261 (5560): 459–467. doi:10.1038/261459a0.</p>
<p>x(t) = r * x(t-1) * (1 - x(t -1) )</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.logistic_map.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.logistic_map.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">r</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span></span><a class="reference internal" href="_modules/pyFTS/data/logistic_map.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.logistic_map.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Return a list with the logistic map chaotic time series.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>r</strong> – Equation coefficient</p></li>
<li><p><strong>initial_value</strong> – Initial value of x. Default: 0.3</p></li>
<li><p><strong>iterations</strong> – number of iterations. Default: 100</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.lorentz">
<span id="lorentz-chaotic-time-series"></span><h2>Lorentz chaotic time series<a class="headerlink" href="#module-pyFTS.data.lorentz" title="Permalink to this headline"></a></h2>
<p>Lorenz, Edward Norton (1963). “Deterministic nonperiodic flow”. Journal of the Atmospheric Sciences. 20 (2): 130–141.
<a class="reference external" href="https://doi.org/10.1175/1520-0469(1963">https://doi.org/10.1175/1520-0469(1963</a>)020&lt;0130:DNF&gt;2.0.CO;2</p>
<p>dx/dt = a(y -x)
dy/dt = x(b - z) - y
dz/dt = xy - cz</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.lorentz.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.lorentz.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">28.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2.6666666666666665</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[0.1,</span> <span class="pre">0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/lorentz.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.lorentz.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>var</strong> – the dataset field name to extract</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.lorentz.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.lorentz.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">28.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2.6666666666666665</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[0.1,</span> <span class="pre">0,</span> <span class="pre">0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/lorentz.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.lorentz.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Return a dataframe with the multivariate Lorenz Map time series (x, y, z).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>a</strong> – Equation coefficient. Default value: 10</p></li>
<li><p><strong>b</strong> – Equation coefficient. Default value: 28</p></li>
<li><p><strong>c</strong> – Equation coefficient. Default value: 8.0/3.0</p></li>
<li><p><strong>dt</strong> – Time differential for continuous time integration. Default value: 0.01</p></li>
<li><p><strong>initial_values</strong> – numpy array with the initial values of x,y and z. Default: [0.1, 0, 0]</p></li>
<li><p><strong>iterations</strong> – number of iterations. Default: 1000</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Panda dataframe with the x, y and z values</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.mackey_glass">
<span id="mackey-glass-chaotic-time-series"></span><h2>Mackey-Glass chaotic time series<a class="headerlink" href="#module-pyFTS.data.mackey_glass" title="Permalink to this headline"></a></h2>
<p>Mackey, M. C. and Glass, L. (1977). Oscillation and chaos in physiological control systems.
Science, 197(4300):287-289.</p>
<p>dy/dt = -by(t)+ cy(t - tau) / 1+y(t-tau)^10</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.mackey_glass.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.mackey_glass.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">17</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">array([0.5,</span> <span class="pre">0.55882353,</span> <span class="pre">0.61764706,</span> <span class="pre">0.67647059,</span> <span class="pre">0.73529412,</span> <span class="pre">0.79411765,</span> <span class="pre">0.85294118,</span> <span class="pre">0.91176471,</span> <span class="pre">0.97058824,</span> <span class="pre">1.02941176,</span> <span class="pre">1.08823529,</span> <span class="pre">1.14705882,</span> <span class="pre">1.20588235,</span> <span class="pre">1.26470588,</span> <span class="pre">1.32352941,</span> <span class="pre">1.38235294,</span> <span class="pre">1.44117647,</span> <span class="pre">1.5])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span></span><a class="reference internal" href="_modules/pyFTS/data/mackey_glass.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.mackey_glass.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Return a list with the Mackey-Glass chaotic time series.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>b</strong> – Equation coefficient</p></li>
<li><p><strong>c</strong> – Equation coefficient</p></li>
<li><p><strong>tau</strong> – Lag parameter, default: 17</p></li>
<li><p><strong>initial_values</strong> – numpy array with the initial values of y. Default: np.linspace(0.5,1.5,18)</p></li>
<li><p><strong>iterations</strong> – number of iterations. Default: 1000</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.rossler">
<span id="rossler-chaotic-time-series"></span><h2>Rossler chaotic time series<a class="headerlink" href="#module-pyFTS.data.rossler" title="Permalink to this headline"></a></h2>
<ol class="upperalpha simple" start="15">
<li><ol class="upperalpha simple" start="5">
<li><p>Rössler, Phys. Lett. 57A, 397 (1976).</p></li>
</ol>
</li>
</ol>
<p>dx/dt = -z - y
dy/dt = x + ay
dz/dt = b + z( x - c )</p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.rossler.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.rossler.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.11)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[0.001,</span> <span class="pre">0.001,</span> <span class="pre">0.001]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5000</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/rossler.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.rossler.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>var</strong> – the dataset field name to extract</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.rossler.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.rossler.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5.7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.11)"><span class="pre">float</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[0.001,</span> <span class="pre">0.001,</span> <span class="pre">0.001]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.11)"><span class="pre">int</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5000</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/rossler.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.rossler.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Return a dataframe with the multivariate Rössler Map time series (x, y, z).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>a</strong> – Equation coefficient. Default value: 0.2</p></li>
<li><p><strong>b</strong> – Equation coefficient. Default value: 0.2</p></li>
<li><p><strong>c</strong> – Equation coefficient. Default value: 5.7</p></li>
<li><p><strong>dt</strong> – Time differential for continuous time integration. Default value: 0.01</p></li>
<li><p><strong>initial_values</strong> – numpy array with the initial values of x,y and z. Default: [0.001, 0.001, 0.001]</p></li>
<li><p><strong>iterations</strong> – number of iterations. Default: 5000</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Panda dataframe with the x, y and z values</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-pyFTS.data.sunspots">
<span id="sunspots-dataset"></span><h2>Sunspots dataset<a class="headerlink" href="#module-pyFTS.data.sunspots" title="Permalink to this headline"></a></h2>
<p>Monthly sunspot numbers from 1749 to May 2016</p>
<p>Source: <a class="reference external" href="https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/SUNSPOT/">https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/SUNSPOT/</a></p>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.sunspots.get_data">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.sunspots.</span></span><span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="reference internal" href="_modules/pyFTS/data/sunspots.html#get_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.sunspots.get_data" title="Permalink to this definition"></a></dt>
<dd><p>Get a simple univariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>numpy array</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="pyFTS.data.sunspots.get_dataframe">
<span class="sig-prename descclassname"><span class="pre">pyFTS.data.sunspots.</span></span><span class="sig-name descname"><span class="pre">get_dataframe</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pandas.core.frame.DataFrame</span></span></span><a class="reference internal" href="_modules/pyFTS/data/sunspots.html#get_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.data.sunspots.get_dataframe" title="Permalink to this definition"></a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Pandas DataFrame</p>
</dd>
</dl>
</dd></dl>
</section>
</section>
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<h3><a href="index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">pyFTS.data package</a><ul>
<li><a class="reference internal" href="#module-pyFTS.data">Module contents</a></li>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.common">pyFTS.data.common module</a></li>
<li><a class="reference internal" href="#datasets">Datasets</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.artificial">Artificial and synthetic data generators</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.AirPassengers">AirPassengers dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.Bitcoin">Bitcoin dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.DowJones">DowJones dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.Enrollments">Enrollments dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.Ethereum">Ethereum dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.EURGBP">EUR-GBP dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.EURUSD">EUR-USD dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.GBPUSD">GBP-USD dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.INMET">INMET dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.Malaysia">Malaysia dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.NASDAQ">NASDAQ module</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.SONDA">SONDA dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.SP500">S&amp;P 500 dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.TAIEX">TAIEX dataset</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.henon">Henon chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.logistic_map">Logistic_map chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.lorentz">Lorentz chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.mackey_glass">Mackey-Glass chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.rossler">Rossler chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.sunspots">Sunspots dataset</a></li>
</ul>
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