<!doctype html> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /><script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-55120145-3']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 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title="pyFTS.models package" href="pyFTS.models.html" /> <link rel="prev" title="pyFTS.common package" href="pyFTS.common.html" /> <meta name="viewport" content="width=device-width,initial-scale=1.0"> <!--[if lt IE 9]> <script type="text/javascript" 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.models.html" title="pyFTS.models package" accesskey="N">next</a> |</li> <li class="right" > <a href="pyFTS.common.html" title="pyFTS.common package" accesskey="P">previous</a> |</li> <li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.2.3 documentation</a> »</li> <li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> »</li> <li class="nav-item nav-item-2"><a href="pyFTS.html" accesskey="U">pyFTS package</a> »</li> </ul> </div> <div class="sphinxsidebar" role="navigation" aria-label="main navigation"> <div class="sphinxsidebarwrapper"> <p class="logo"><a href="index.html"> <img class="logo" src="_static/logo_heading2.png" alt="Logo"/> </a></p> <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.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&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> </li> </ul> <h4>Previous topic</h4> <p class="topless"><a href="pyFTS.common.html" title="previous chapter">pyFTS.common package</a></p> <h4>Next topic</h4> <p class="topless"><a href="pyFTS.models.html" title="next chapter">pyFTS.models 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>Quick search</h3> <div class="searchformwrapper"> <form class="search" action="search.html" method="get"> <input type="text" name="q" /> <input type="submit" value="Go" /> <input type="hidden" name="check_keywords" value="yes" /> <input type="hidden" name="area" value="default" /> </form> </div> </div> <script type="text/javascript">$('#searchbox').show(0);</script> </div> </div> <div class="document"> <div class="documentwrapper"> <div class="bodywrapper"> <div class="body" role="main"> <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&selected=IXIC">http://www.nasdaq.com/aspx/flashquotes.aspx?symbol=IXIC&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&P 500 dataset<a class="headerlink" href="#module-pyFTS.data.SP500" title="Permalink to this headline">¶</a></h2> <p>S&P500 - Standard & 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<0130:DNF>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> </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.models.html" title="pyFTS.models package" >next</a> |</li> <li class="right" > <a href="pyFTS.common.html" title="pyFTS.common package" >previous</a> |</li> <li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.2.3 documentation</a> »</li> <li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> »</li> <li class="nav-item nav-item-2"><a href="pyFTS.html" >pyFTS package</a> »</li> </ul> </div> <div class="footer" role="contentinfo"> © Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil. 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