sam/pyFTS
1
0
Fork 0
Code Issues 3 Pull Requests Actions Packages Projects Releases Wiki Activity
7f6ab34b78
pyFTS/docs/build/html/pyFTS.data.html

1017 lines
108 KiB
HTML
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

<!doctype html>
<html>
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
<title>pyFTS.data package &#8212; pyFTS 1.7 documentation</title>
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
<link rel="stylesheet" type="text/css" href="_static/bizstyle.css" />
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script>
<script src="_static/underscore.js"></script>
<script src="_static/doctools.js"></script>
<script src="_static/bizstyle.js"></script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="pyFTS.distributed package" href="pyFTS.distributed.html" />
<link rel="prev" title="pyFTS.common.transformations package" href="pyFTS.common.transformations.html" />
<meta name="viewport" content="width=device-width,initial-scale=1.0" />
<!--[if lt IE 9]>
<script src="_static/css3-mediaqueries.js"></script>
<![endif]-->
</head><body>
<div class="related" role="navigation" aria-label="related navigation">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="genindex.html" title="General Index"
accesskey="I">index</a></li>
<li class="right" >
<a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="right" >
<a href="pyFTS.distributed.html" title="pyFTS.distributed package"
accesskey="N">next</a> |</li>
<li class="right" >
<a href="pyFTS.common.transformations.html" title="pyFTS.common.transformations package"
accesskey="P">previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> &#187;</li>
<li class="nav-item nav-item-2"><a href="pyFTS.html" accesskey="U">pyFTS package</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">pyFTS.data package</a></li>
</ul>
</div>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<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>
<div class="clearer"></div>
</div>
</div>
</div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper">
<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>
</li>
</ul>
<h4>Previous topic</h4>
<p class="topless"><a href="pyFTS.common.transformations.html"
title="previous chapter">pyFTS.common.transformations package</a></p>
<h4>Next topic</h4>
<p class="topless"><a href="pyFTS.distributed.html"
title="next chapter">pyFTS.distributed package</a></p>
<div role="note" aria-label="source link">
<h3>This Page</h3>
<ul class="this-page-menu">
<li><a href="_sources/pyFTS.data.rst.txt"
rel="nofollow">Show Source</a></li>
</ul>
</div>
<div id="searchbox" style="display: none" role="search">
<h3 id="searchlabel">Quick search</h3>
<div class="searchformwrapper">
<form class="search" action="search.html" method="get">
<input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
<input type="submit" value="Go" />
</form>
</div>
</div>
<script>$('#searchbox').show(0);</script>
</div>
</div>
<div class="clearer"></div>
</div>
<div class="related" role="navigation" aria-label="related navigation">
<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="genindex.html" title="General Index"
>index</a></li>
<li class="right" >
<a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="right" >
<a href="pyFTS.distributed.html" title="pyFTS.distributed package"
>next</a> |</li>
<li class="right" >
<a href="pyFTS.common.transformations.html" title="pyFTS.common.transformations package"
>previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> &#187;</li>
<li class="nav-item nav-item-2"><a href="pyFTS.html" >pyFTS package</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">pyFTS.data package</a></li>
</ul>
</div>
<div class="footer" role="contentinfo">
&#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
</div>
</body>
</html>
Reference in New Issue View Git Blame Copy Permalink