<spanid="pyfts-data-airpassengers-module"></span><h2>pyFTS.data.AirPassengers module<aclass="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, <aclass="reference external"href="http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/">http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/</a>.</p>
<codeclass="descclassname">pyFTS.data.AirPassengers.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/AirPassengers.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.AirPassengers.get_data"title="Permalink to this definition">¶</a></dt>
<dd><p>Get a simple univariate time series data.</p>
<codeclass="descclassname">pyFTS.data.AirPassengers.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/AirPassengers.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.AirPassengers.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<spanid="pyfts-data-enrollments-module"></span><h2>pyFTS.data.Enrollments module<aclass="headerlink"href="#module-pyFTS.data.Enrollments"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.data.Enrollments.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/Enrollments.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.Enrollments.get_data"title="Permalink to this definition">¶</a></dt>
<dd><p>Get a simple univariate time series data.</p>
<codeclass="descclassname">pyFTS.data.Enrollments.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/Enrollments.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.Enrollments.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</div>
<divclass="section"id="module-pyFTS.data.INMET">
<spanid="pyfts-data-inmet-module"></span><h2>pyFTS.data.INMET module<aclass="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>
<codeclass="descclassname">pyFTS.data.INMET.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/INMET.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.INMET.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<spanid="pyfts-data-nasdaq-module"></span><h2>pyFTS.data.NASDAQ module<aclass="headerlink"href="#module-pyFTS.data.NASDAQ"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.data.NASDAQ.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><em>field='avg'</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/NASDAQ.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.NASDAQ.get_data"title="Permalink to this definition">¶</a></dt>
<dd><p>Get a simple univariate time series data.</p>
<codeclass="descclassname">pyFTS.data.NASDAQ.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/NASDAQ.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.NASDAQ.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<spanid="pyfts-data-sonda-module"></span><h2>pyFTS.data.SONDA module<aclass="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>
<codeclass="descclassname">pyFTS.data.SONDA.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><em>field</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/SONDA.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.SONDA.get_data"title="Permalink to this definition">¶</a></dt>
<dd><p>Get a simple univariate time series data.</p>
<codeclass="descclassname">pyFTS.data.SONDA.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/SONDA.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.SONDA.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<spanid="pyfts-data-sp500-module"></span><h2>pyFTS.data.SP500 module<aclass="headerlink"href="#module-pyFTS.data.SP500"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.data.SP500.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/SP500.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.SP500.get_data"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.data.SP500.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/SP500.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.SP500.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<spanid="pyfts-data-taiex-module"></span><h2>pyFTS.data.TAIEX module<aclass="headerlink"href="#module-pyFTS.data.TAIEX"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.data.TAIEX.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/TAIEX.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.TAIEX.get_data"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.data.TAIEX.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/TAIEX.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.TAIEX.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd><p>Get the complete multivariate time series data.</p>
<spanid="pyfts-data-artificial-module"></span><h2>pyFTS.data.artificial module<aclass="headerlink"href="#module-pyFTS.data.artificial"title="Permalink to this headline">¶</a></h2>
<p>Facilities to generate synthetic stochastic processes</p>
<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>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">A list of it*num float values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dlclass="function">
<dtid="pyFTS.data.artificial.random_walk">
<codeclass="descclassname">pyFTS.data.artificial.</code><codeclass="descname">random_walk</code><spanclass="sig-paren">(</span><em>n=500</em>, <em>type='gaussian'</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/artificial.html#random_walk"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.artificial.random_walk"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="function">
<dtid="pyFTS.data.artificial.white_noise">
<codeclass="descclassname">pyFTS.data.artificial.</code><codeclass="descname">white_noise</code><spanclass="sig-paren">(</span><em>n=500</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/artificial.html#white_noise"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.artificial.white_noise"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</div>
<divclass="section"id="module-pyFTS.data.common">
<spanid="pyfts-data-common-module"></span><h2>pyFTS.data.common module<aclass="headerlink"href="#module-pyFTS.data.common"title="Permalink to this headline">¶</a></h2>
<dlclass="function">
<dtid="pyFTS.data.common.get_dataframe">
<codeclass="descclassname">pyFTS.data.common.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><em>filename</em>, <em>url</em>, <em>sep=';'</em>, <em>compression='infer'</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/common.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="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>
<spanid="pyfts-data-henon-module"></span><h2>pyFTS.data.henon module<aclass="headerlink"href="#module-pyFTS.data.henon"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.data.henon.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><em>var, a=1.4, b=0.3, initial_values=[1, 1], iterations=1000</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/henon.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.henon.get_data"title="Permalink to this definition">¶</a></dt>
<codeclass="descclassname">pyFTS.data.henon.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><em>a=1.4, b=0.3, initial_values=[1, 1], iterations=1000</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/henon.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.henon.get_dataframe"title="Permalink to this definition">¶</a></dt>
<spanid="pyfts-data-logistic-map-module"></span><h2>pyFTS.data.logistic_map module<aclass="headerlink"href="#module-pyFTS.data.logistic_map"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.data.logistic_map.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><em>r=4</em>, <em>initial_value=0.3</em>, <em>iterations=100</em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/logistic_map.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.logistic_map.get_data"title="Permalink to this definition">¶</a></dt>
<spanid="pyfts-data-lorentz-module"></span><h2>pyFTS.data.lorentz module<aclass="headerlink"href="#module-pyFTS.data.lorentz"title="Permalink to this headline">¶</a></h2>
<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>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">Panda dataframe with the x, y and z values</p>
<spanid="pyfts-data-mackey-glass-module"></span><h2>pyFTS.data.mackey_glass module<aclass="headerlink"href="#module-pyFTS.data.mackey_glass"title="Permalink to this headline">¶</a></h2>
<spanid="pyfts-data-rossler-module"></span><h2>pyFTS.data.rossler module<aclass="headerlink"href="#module-pyFTS.data.rossler"title="Permalink to this headline">¶</a></h2>
<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>
<trclass="field-even field"><thclass="field-name">Returns:</th><tdclass="field-body"><pclass="first last">Panda dataframe with the x, y and z values</p>
<spanid="pyfts-data-sunspots-module"></span><h2>pyFTS.data.sunspots module<aclass="headerlink"href="#module-pyFTS.data.sunspots"title="Permalink to this headline">¶</a></h2>
<codeclass="descclassname">pyFTS.data.sunspots.</code><codeclass="descname">get_data</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/sunspots.html#get_data"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.sunspots.get_data"title="Permalink to this definition">¶</a></dt>
<dd><p>Get a simple univariate time series data.</p>
<codeclass="descclassname">pyFTS.data.sunspots.</code><codeclass="descname">get_dataframe</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/pyFTS/data/sunspots.html#get_dataframe"><spanclass="viewcode-link">[source]</span></a><aclass="headerlink"href="#pyFTS.data.sunspots.get_dataframe"title="Permalink to this definition">¶</a></dt>
<dd><p>Get the complete multivariate time series data.</p>