Documentation update

docs/build/html/_sources/quickstart.rst.txt

@@ -25,9 +25,9 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
 2. **Universe of Discourse Partitioning**: This is the most important step. Here, the range of values of the numerical time series *Y(t)* will be splited in overlapped intervals and for each interval will be created a Fuzzy Set. This step is performed by pyFTS.partition module and its classes (for instance GridPartitioner, EntropyPartitioner, etc). The main parameters are:
  - the number of intervals
  - which fuzzy membership function (on `pyFTS.common.Membership <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py>`_)
- - partition scheme (`GridPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py>`_, `EntropyPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py>`_, `FCMPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py>`_, `CMeansPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py>`_, `HuarngPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py>`_)
+ - partition scheme (`GridPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py>`_, `EntropyPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py>`_, `FCMPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py>`_, `HuarngPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py>`_)
  
- Check out the jupyter notebook on `notebooks/Partitioners.ipynb <https://github.com/PYFTS/notebooks/Partitioners.ipynb>`_ for sample codes.
+ Check out the jupyter notebook on `notebooks/Partitioners.ipynb <https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb>`_ for sample codes.
  
 3. **Data Fuzzyfication**: Each data point of the numerical time series *Y(t)* will be translated to a fuzzy representation (usually one or more fuzzy sets), and then a fuzzy time series *F(t)* is created.
 

docs/build/html/genindex.html

@@ -656,8 +656,6 @@
         <li><a href="pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">(pyFTS.models.ifts.IntervalFTS method)</a>
 </li>
         <li><a href="pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">(pyFTS.models.ifts.WeightedIntervalFTS method)</a>
-</li>
-        <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_interval">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS method)</a>
 </li>
         <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">(pyFTS.models.multivariate.mvfts.MVFTS method)</a>
 </li>
@@ -694,6 +692,8 @@
         <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
 </li>
       </ul></li>
+      <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution">forecast_distribution_from_distribution() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
+</li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_interval">forecast_interval() (pyFTS.benchmarks.arima.ARIMA method)</a>
 
       <ul>
@@ -739,6 +739,8 @@
 </li>
       </ul></li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.format_experiment_table">format_experiment_table() (in module pyFTS.benchmarks.Tests)</a>
+</li>
+      <li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.from_point">from_point() (in module pyFTS.probabilistic.ProbabilityDistribution)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS">FTS (class in pyFTS.common.fts)</a>
 </li>
@@ -1346,12 +1348,12 @@
       </ul></li>
       <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.merge">merge() (pyFTS.common.fts.FTS method)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute">minute (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_day">minute_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_month">minute_of_month (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_week">minute_of_week (pyFTS.models.seasonal.common.DateTime attribute)</a>
@@ -1361,10 +1363,18 @@
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.month">month (pyFTS.models.seasonal.common.DateTime attribute)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS">MultiSeasonalFTS (class in pyFTS.models.seasonal.msfts)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">multivariate_sliding_window_benchmarks2() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet">MultivariateFuzzySet (class in pyFTS.models.multivariate.common)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner">MultivariatePartitioner (class in pyFTS.models.multivariate.partitioner)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_interval2">mv_run_interval2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_point2">mv_run_point2() (in module pyFTS.benchmarks.benchmarks)</a>
+</li>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">mv_run_probabilistic2() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS">MVFTS (class in pyFTS.models.multivariate.mvfts)</a>
 </li>
@@ -1471,6 +1481,8 @@
       <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_distribution">plot_distribution() (in module pyFTS.common.Util)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_distribution2">plot_distribution2() (in module pyFTS.common.Util)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_distribution_tiled">plot_distribution_tiled() (in module pyFTS.common.Util)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Util.plot_interval">plot_interval() (in module pyFTS.common.Util)</a>
 </li>

docs/build/html/pyFTS.benchmarks.html

@@ -202,6 +202,26 @@
 <dd><p>Return all FTS methods for probabilistic forecasting</p>
 </dd></dl>
 
+<dl class="function">
+<dt id="pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2">
+<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">multivariate_sliding_window_benchmarks2</code><span class="sig-paren">(</span><em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.multivariate_sliding_window_benchmarks2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="function">
+<dt id="pyFTS.benchmarks.benchmarks.mv_run_interval2">
+<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">mv_run_interval2</code><span class="sig-paren">(</span><em>mfts</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_interval2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="function">
+<dt id="pyFTS.benchmarks.benchmarks.mv_run_point2">
+<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">mv_run_point2</code><span class="sig-paren">(</span><em>mfts</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_point2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="function">
+<dt id="pyFTS.benchmarks.benchmarks.mv_run_probabilistic2">
+<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">mv_run_probabilistic2</code><span class="sig-paren">(</span><em>mfts</em>, <em>train_data</em>, <em>test_data</em>, <em>window_key=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.mv_run_probabilistic2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 <dl class="function">
 <dt id="pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions">
 <code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">pftsExploreOrderAndPartitions</code><span class="sig-paren">(</span><em>data</em>, <em>save=False</em>, <em>file=None</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.pftsExploreOrderAndPartitions" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.common.html

@@ -1253,7 +1253,7 @@ bisect but with a simpler API and support for key functions.</p>
 <dl class="function">
 <dt id="pyFTS.common.Util.plot_distribution2">
 <code class="descclassname">pyFTS.common.Util.</code><code class="descname">plot_distribution2</code><span class="sig-paren">(</span><em>probabilitydist</em>, <em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Util.plot_distribution2" title="Permalink to this definition">¶</a></dt>
-<dd><p>Plot distributions over the time (x-axis)</p>
+<dd><p>Plot distributions in y-axis over the time (x-axis)</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
@@ -1273,6 +1273,32 @@ bisect but with a simpler API and support for key functions.</p>
 </table>
 </dd></dl>
 
+<dl class="function">
+<dt id="pyFTS.common.Util.plot_distribution_tiled">
+<code class="descclassname">pyFTS.common.Util.</code><code class="descname">plot_distribution_tiled</code><span class="sig-paren">(</span><em>distributions, data=None, rows=5, cols=5, index=None, axis=None, size=[10, 20]</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Util.plot_distribution_tiled" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot one distribution individually in each axis, with probability in y-axis and UoD on x-axis</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>distributions</strong> – </li>
+<li><strong>data</strong> – </li>
+<li><strong>rows</strong> – </li>
+<li><strong>cols</strong> – </li>
+<li><strong>index</strong> – </li>
+<li><strong>axis</strong> – </li>
+<li><strong>size</strong> – </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>
+
 <dl class="function">
 <dt id="pyFTS.common.Util.plot_interval">
 <code class="descclassname">pyFTS.common.Util.</code><code class="descname">plot_interval</code><span class="sig-paren">(</span><em>axis</em>, <em>intervals</em>, <em>order</em>, <em>label</em>, <em>color='red'</em>, <em>typeonlegend=False</em>, <em>ls='-'</em>, <em>linewidth=1</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Util.plot_interval" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.hyperparam.html

@@ -115,7 +115,7 @@
 </div>
 <div class="section" id="module-pyFTS.hyperparam.Util">
 <span id="pyfts-hyperparam-util-module"></span><h2>pyFTS.hyperparam.Util module<a class="headerlink" href="#module-pyFTS.hyperparam.Util" title="Permalink to this headline">¶</a></h2>
-<p>Common facilities for hyperparameter tunning</p>
+<p>Common facilities for hyperparameter optimization</p>
 <dl class="function">
 <dt id="pyFTS.hyperparam.Util.create_hyperparam_tables">
 <code class="descclassname">pyFTS.hyperparam.Util.</code><code class="descname">create_hyperparam_tables</code><span class="sig-paren">(</span><em>conn</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.hyperparam.Util.create_hyperparam_tables" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.models.html

@@ -1101,6 +1101,11 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
 </table>
 </dd></dl>
 
+<dl class="method">
+<dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution">
+<code class="descname">forecast_distribution_from_distribution</code><span class="sig-paren">(</span><em>previous_dist</em>, <em>smooth</em>, <em>uod</em>, <em>bins</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 <dl class="method">
 <dt id="pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">
 <code class="descname">forecast_interval</code><span class="sig-paren">(</span><em>ndata</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.models.multivariate.html

@@ -806,28 +806,6 @@ multivariate fuzzy set base.</p>
 </table>
 </dd></dl>
 
-<dl class="method">
-<dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_interval">
-<code class="descname">forecast_ahead_interval</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_interval" title="Permalink to this definition">¶</a></dt>
-<dd><p>Interval forecast n steps ahead</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>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
-<li><strong>steps</strong> – the number of steps ahead to forecast</li>
-<li><strong>start_at</strong> – in the multi step forecasting, the index of the data where to start forecasting (default: 0)</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 with the forecasted intervals</p>
-</td>
-</tr>
-</tbody>
-</table>
-</dd></dl>
-
 <dl class="method">
 <dt id="pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">
 <code class="descname">forecast_ahead_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>steps</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.probabilistic.html

@@ -340,6 +340,27 @@ such that Q(tau) = min( {x | F(x) &gt;= tau })</p>
 
 </dd></dl>
 
+<dl class="function">
+<dt id="pyFTS.probabilistic.ProbabilityDistribution.from_point">
+<code class="descclassname">pyFTS.probabilistic.ProbabilityDistribution.</code><code class="descname">from_point</code><span class="sig-paren">(</span><em>x</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.from_point" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a probability distribution from a scalar value</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>x</strong> – scalar value</li>
+<li><strong>kwargs</strong> – common parameters of the distribution</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the ProbabilityDistribution object</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
 </div>
 <div class="section" id="module-pyFTS.probabilistic.kde">
 <span id="pyfts-probabilistic-kde-module"></span><h2>pyFTS.probabilistic.kde module<a class="headerlink" href="#module-pyFTS.probabilistic.kde" title="Permalink to this headline">¶</a></h2>

docs/build/html/quickstart.html

@@ -124,9 +124,9 @@
 <div><ul class="simple">
 <li>the number of intervals</li>
 <li>which fuzzy membership function (on <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py">pyFTS.common.Membership</a>)</li>
-<li>partition scheme (<a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py">GridPartitioner</a>, <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py">EntropyPartitioner</a>, <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py">FCMPartitioner</a>, <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py">CMeansPartitioner</a>, <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py">HuarngPartitioner</a>)</li>
+<li>partition scheme (<a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py">GridPartitioner</a>, <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py">EntropyPartitioner</a>, <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py">FCMPartitioner</a>, <a class="reference external" href="https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py">HuarngPartitioner</a>)</li>
 </ul>
-<p>Check out the jupyter notebook on <a class="reference external" href="https://github.com/PYFTS/notebooks/Partitioners.ipynb">notebooks/Partitioners.ipynb</a> for sample codes.</p>
+<p>Check out the jupyter notebook on <a class="reference external" href="https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb">notebooks/Partitioners.ipynb</a> for sample codes.</p>
 </div></blockquote>
 <ol class="arabic simple" start="3">
 <li><strong>Data Fuzzyfication</strong>: Each data point of the numerical time series <em>Y(t)</em> will be translated to a fuzzy representation (usually one or more fuzzy sets), and then a fuzzy time series <em>F(t)</em> is created.</li>

docs/quickstart.rst

@@ -25,9 +25,9 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
 2. **Universe of Discourse Partitioning**: This is the most important step. Here, the range of values of the numerical time series *Y(t)* will be splited in overlapped intervals and for each interval will be created a Fuzzy Set. This step is performed by pyFTS.partition module and its classes (for instance GridPartitioner, EntropyPartitioner, etc). The main parameters are:
  - the number of intervals
  - which fuzzy membership function (on `pyFTS.common.Membership <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py>`_)
- - partition scheme (`GridPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py>`_, `EntropyPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py>`_, `FCMPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py>`_, `CMeansPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py>`_, `HuarngPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py>`_)
+ - partition scheme (`GridPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py>`_, `EntropyPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py>`_, `FCMPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py>`_, `HuarngPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py>`_)
  
- Check out the jupyter notebook on `notebooks/Partitioners.ipynb <https://github.com/PYFTS/notebooks/Partitioners.ipynb>`_ for sample codes.
+ Check out the jupyter notebook on `notebooks/Partitioners.ipynb <https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb>`_ for sample codes.
  
 3. **Data Fuzzyfication**: Each data point of the numerical time series *Y(t)* will be translated to a fuzzy representation (usually one or more fuzzy sets), and then a fuzzy time series *F(t)* is created.