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<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.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.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.artificial">pyFTS.data.artificial module</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.henon">Henon chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.logistic_map">Logistic_map chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.lorentz">Lorentz chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.mackey_glass">Mackey-Glass chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.rossler">Rossler chaotic time series</a></li>
<li><a class="reference internal" href="#module-pyFTS.data.sunspots">Sunspots dataset</a></li>
</ul>
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<div class="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>
<div class="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>
</div>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
<div class="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="function">
<dt id="pyFTS.data.common.get_dataframe">
<code class="descclassname">pyFTS.data.common.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><em>filename</em>, <em>url</em>, <em>sep=';'</em>, <em>compression='infer'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/common.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>filename</strong> – dataset local filename</li>
<li><strong>url</strong> – dataset internet URL</li>
<li><strong>sep</strong> – CSV field separator</li>
<li><strong>compression</strong> – type of compression</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Pandas dataset</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="datasets">
<h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline"></a></h2>
</div>
<div class="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="function">
<dt id="pyFTS.data.AirPassengers.get_data">
<code class="descclassname">pyFTS.data.AirPassengers.</code><code class="descname">get_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/AirPassengers.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.AirPassengers.get_dataframe">
<code class="descclassname">pyFTS.data.AirPassengers.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/AirPassengers.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.Bitcoin.get_data">
<code class="descclassname">pyFTS.data.Bitcoin.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field='AVG'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/Bitcoin.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – dataset field to load</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.Bitcoin.get_dataframe">
<code class="descclassname">pyFTS.data.Bitcoin.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/Bitcoin.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.DowJones.get_data">
<code class="descclassname">pyFTS.data.DowJones.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field='AVG'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/DowJones.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – dataset field to load</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.DowJones.get_dataframe">
<code class="descclassname">pyFTS.data.DowJones.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/DowJones.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.Enrollments.get_data">
<code class="descclassname">pyFTS.data.Enrollments.</code><code class="descname">get_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/Enrollments.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.Enrollments.get_dataframe">
<code class="descclassname">pyFTS.data.Enrollments.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/Enrollments.html#get_dataframe"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.Enrollments.get_dataframe" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.Ethereum.get_data">
<code class="descclassname">pyFTS.data.Ethereum.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field='AVG'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/Ethereum.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – dataset field to load</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.Ethereum.get_dataframe">
<code class="descclassname">pyFTS.data.Ethereum.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/Ethereum.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.EURGBP.get_data">
<code class="descclassname">pyFTS.data.EURGBP.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/EURGBP.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – dataset field to load</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.EURGBP.get_dataframe">
<code class="descclassname">pyFTS.data.EURGBP.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/EURGBP.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.EURUSD.get_data">
<code class="descclassname">pyFTS.data.EURUSD.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/EURUSD.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – dataset field to load</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.EURUSD.get_dataframe">
<code class="descclassname">pyFTS.data.EURUSD.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/EURUSD.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.GBPUSD.get_data">
<code class="descclassname">pyFTS.data.GBPUSD.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/GBPUSD.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – dataset field to load</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.GBPUSD.get_dataframe">
<code class="descclassname">pyFTS.data.GBPUSD.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/GBPUSD.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.INMET.get_dataframe">
<code class="descclassname">pyFTS.data.INMET.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/INMET.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.NASDAQ.get_data">
<code class="descclassname">pyFTS.data.NASDAQ.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/NASDAQ.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – the dataset field name to extract</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.NASDAQ.get_dataframe">
<code class="descclassname">pyFTS.data.NASDAQ.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/NASDAQ.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.SONDA.get_data">
<code class="descclassname">pyFTS.data.SONDA.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>field</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/SONDA.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>field</strong> – the dataset field name to extract</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.SONDA.get_dataframe">
<code class="descclassname">pyFTS.data.SONDA.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/SONDA.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.SP500.get_data">
<code class="descclassname">pyFTS.data.SP500.</code><code class="descname">get_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/SP500.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.SP500.get_dataframe">
<code class="descclassname">pyFTS.data.SP500.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/SP500.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.TAIEX.get_data">
<code class="descclassname">pyFTS.data.TAIEX.</code><code class="descname">get_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/TAIEX.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.TAIEX.get_dataframe">
<code class="descclassname">pyFTS.data.TAIEX.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/TAIEX.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="module-pyFTS.data.artificial">
<span id="pyfts-data-artificial-module"></span><h2>pyFTS.data.artificial module<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="function">
<dt id="pyFTS.data.artificial.generate_gaussian_linear">
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_gaussian_linear</code><span class="sig-paren">(</span><em>mu_ini</em>, <em>sigma_ini</em>, <em>mu_inc</em>, <em>sigma_inc</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_gaussian_linear"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>mu_ini</strong> – Initial mean</li>
<li><strong>sigma_ini</strong> – Initial variance</li>
<li><strong>mu_inc</strong> – Mean increment after ‘num’ samples</li>
<li><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</li>
<li><strong>it</strong> – Number of iterations</li>
<li><strong>num</strong> – Number of samples generated on each iteration</li>
<li><strong>vmin</strong> – Lower bound value of generated data</li>
<li><strong>vmax</strong> – Upper bound value of generated data</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.artificial.generate_uniform_linear">
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_uniform_linear</code><span class="sig-paren">(</span><em>min_ini</em>, <em>max_ini</em>, <em>min_inc</em>, <em>max_inc</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_uniform_linear"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>mu_ini</strong> – Initial mean</li>
<li><strong>sigma_ini</strong> – Initial variance</li>
<li><strong>mu_inc</strong> – Mean increment after ‘num’ samples</li>
<li><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</li>
<li><strong>it</strong> – Number of iterations</li>
<li><strong>num</strong> – Number of samples generated on each iteration</li>
<li><strong>vmin</strong> – Lower bound value of generated data</li>
<li><strong>vmax</strong> – Upper bound value of generated data</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.artificial.random_walk">
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">random_walk</code><span class="sig-paren">(</span><em>n=500</em>, <em>type='gaussian'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#random_walk"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.random_walk" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="function">
<dt id="pyFTS.data.artificial.white_noise">
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">white_noise</code><span class="sig-paren">(</span><em>n=500</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#white_noise"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.white_noise" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</div>
<div class="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>Hénon. “A two-dimensional mapping with a strange attractor”. Commun. Math. Phys. 50, 69-77 (1976)</li>
</ol>
<p>dx/dt = a + by(t-1) - x(t-1)^2
dy/dt = x</p>
<dl class="function">
<dt id="pyFTS.data.henon.get_data">
<code class="descclassname">pyFTS.data.henon.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>var, a=1.4, b=0.3, initial_values=[1, 1], iterations=1000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/henon.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>var</strong> – the dataset field name to extract</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.henon.get_dataframe">
<code class="descclassname">pyFTS.data.henon.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><em>a=1.4, b=0.3, initial_values=[1, 1], iterations=1000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/henon.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>a</strong> – Equation coefficient</li>
<li><strong>b</strong> – Equation coefficient</li>
<li><strong>initial_values</strong> – numpy array with the initial values of x and y. Default: [1, 1]</li>
<li><strong>iterations</strong> – number of iterations. Default: 1000</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Panda dataframe with the x and y values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.logistic_map.get_data">
<code class="descclassname">pyFTS.data.logistic_map.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>r=4</em>, <em>initial_value=0.3</em>, <em>iterations=100</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/logistic_map.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>r</strong> – Equation coefficient</li>
<li><strong>initial_value</strong> – Initial value of x. Default: 0.3</li>
<li><strong>iterations</strong> – number of iterations. Default: 100</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.lorentz.get_data">
<code class="descclassname">pyFTS.data.lorentz.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>var, a=10.0, b=28.0, c=2.6666666666666665, dt=0.01, initial_values=[0.1, 0, 0], iterations=1000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/lorentz.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>var</strong> – the dataset field name to extract</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.lorentz.get_dataframe">
<code class="descclassname">pyFTS.data.lorentz.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><em>a=10.0, b=28.0, c=2.6666666666666665, dt=0.01, initial_values=[0.1, 0, 0], iterations=1000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/lorentz.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>a</strong> – Equation coefficient. Default value: 10</li>
<li><strong>b</strong> – Equation coefficient. Default value: 28</li>
<li><strong>c</strong> – Equation coefficient. Default value: 8.0/3.0</li>
<li><strong>dt</strong> – Time differential for continuous time integration. Default value: 0.01</li>
<li><strong>initial_values</strong> – numpy array with the initial values of x,y and z. Default: [0.1, 0, 0]</li>
<li><strong>iterations</strong> – number of iterations. Default: 1000</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Panda dataframe with the x, y and z values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.mackey_glass.get_data">
<code class="descclassname">pyFTS.data.mackey_glass.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>b=0.1</em>, <em>c=0.2</em>, <em>tau=17</em>, <em>initial_values=array([0.5</em>, <em>0.55882353</em>, <em>0.61764706</em>, <em>0.67647059</em>, <em>0.73529412</em>, <em>0.79411765</em>, <em>0.85294118</em>, <em>0.91176471</em>, <em>0.97058824</em>, <em>1.02941176</em>, <em>1.08823529</em>, <em>1.14705882</em>, <em>1.20588235</em>, <em>1.26470588</em>, <em>1.32352941</em>, <em>1.38235294</em>, <em>1.44117647</em>, <em>1.5 ])</em>, <em>iterations=1000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/mackey_glass.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>b</strong> – Equation coefficient</li>
<li><strong>c</strong> – Equation coefficient</li>
<li><strong>tau</strong> – Lag parameter, default: 17</li>
<li><strong>initial_values</strong> – numpy array with the initial values of y. Default: np.linspace(0.5,1.5,18)</li>
<li><strong>iterations</strong> – number of iterations. Default: 1000</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="first upperalpha" start="5">
<li>Rössler, Phys. Lett. 57A, 397 (1976).</li>
</ol>
</li>
</ol>
<p>dx/dt = -z - y
dy/dt = x + ay
dz/dt = b + z( x - c )</p>
<dl class="function">
<dt id="pyFTS.data.rossler.get_data">
<code class="descclassname">pyFTS.data.rossler.</code><code class="descname">get_data</code><span class="sig-paren">(</span><em>var, a=0.2, b=0.2, c=5.7, dt=0.01, initial_values=[0.001, 0.001, 0.001], iterations=5000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/rossler.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>var</strong> – the dataset field name to extract</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.rossler.get_dataframe">
<code class="descclassname">pyFTS.data.rossler.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><em>a=0.2, b=0.2, c=5.7, dt=0.01, initial_values=[0.001, 0.001, 0.001], iterations=5000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/rossler.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>a</strong> – Equation coefficient. Default value: 0.2</li>
<li><strong>b</strong> – Equation coefficient. Default value: 0.2</li>
<li><strong>c</strong> – Equation coefficient. Default value: 5.7</li>
<li><strong>dt</strong> – Time differential for continuous time integration. Default value: 0.01</li>
<li><strong>initial_values</strong> – numpy array with the initial values of x,y and z. Default: [0.001, 0.001, 0.001]</li>
<li><strong>iterations</strong> – number of iterations. Default: 5000</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Panda dataframe with the x, y and z values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="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="function">
<dt id="pyFTS.data.sunspots.get_data">
<code class="descclassname">pyFTS.data.sunspots.</code><code class="descname">get_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/sunspots.html#get_data"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.data.sunspots.get_dataframe">
<code class="descclassname">pyFTS.data.sunspots.</code><code class="descname">get_dataframe</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/sunspots.html#get_dataframe"><span class="viewcode-link">[source]</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">Pandas DataFrame</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
</div>
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