From e81e64553c84c7b287b070220dcf26c364bcc8be Mon Sep 17 00:00:00 2001
From: Emerald01 <Emerald01@users.noreply.github.com>
Date: Thu, 20 Jun 2024 05:59:42 +0000
Subject: [PATCH] deploy: 032ae63d4f1b2609553515d6cecb3e3c569e64c5

---
 .../_sources/merlion.models.anomaly.rst.txt   |   7 +
 latest/genindex.html                          |  23 ++-
 latest/merlion.models.anomaly.html            | 132 ++++++++++++++++++
 latest/merlion.models.html                    |   1 +
 latest/objects.inv                            | Bin 27347 -> 27454 bytes
 latest/py-modindex.html                       |   5 +
 latest/searchindex.js                         |   2 +-
 7 files changed, 167 insertions(+), 3 deletions(-)

diff --git a/latest/_sources/merlion.models.anomaly.rst.txt b/latest/_sources/merlion.models.anomaly.rst.txt
index fcd17032a..133e1c722 100644
--- a/latest/_sources/merlion.models.anomaly.rst.txt
+++ b/latest/_sources/merlion.models.anomaly.rst.txt
@@ -133,6 +133,13 @@ anomaly.lstm_ed
    :undoc-members:
    :show-inheritance:
 
+anomaly.lof
+^^^^^^^^^^^^^^^
+.. automodule:: merlion.models.anomaly.lof
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 anomaly.deep\_point\_anomaly\_detector
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. automodule:: merlion.models.anomaly.deep_point_anomaly_detector
diff --git a/latest/genindex.html b/latest/genindex.html
index bb9140464..b6051a2f3 100644
--- a/latest/genindex.html
+++ b/latest/genindex.html
@@ -398,6 +398,8 @@ <h2 id="C">C</h2>
         <li><a href="merlion.models.anomaly.forecast_based.html#merlion.models.anomaly.forecast_based.sarima.SarimaDetector.config_class">(merlion.models.anomaly.forecast_based.sarima.SarimaDetector attribute)</a>
 </li>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.isolation_forest.IsolationForest.config_class">(merlion.models.anomaly.isolation_forest.IsolationForest attribute)</a>
+</li>
+        <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lof.LOF.config_class">(merlion.models.anomaly.lof.LOF attribute)</a>
 </li>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lstm_ed.LSTMED.config_class">(merlion.models.anomaly.lstm_ed.LSTMED attribute)</a>
 </li>
@@ -1242,14 +1244,14 @@ <h2 id="L">L</h2>
 </li>
       <li><a href="merlion.models.html#merlion.models.layers.LayeredModelConfig">LayeredModelConfig (class in merlion.models.layers)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="merlion.transform.html#merlion.transform.anomalize.LevelShift">LevelShift (class in merlion.transform.anomalize)</a>
 
       <ul>
         <li><a href="merlion.models.anomaly.change_point.html#merlion.models.anomaly.change_point.bocpd.ChangeKind.LevelShift">(merlion.models.anomaly.change_point.bocpd.ChangeKind attribute)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="merlion.models.forecast.html#merlion.models.forecast.trees.LGBMForecaster">LGBMForecaster (class in merlion.models.forecast.trees)</a>
 </li>
       <li><a href="merlion.models.forecast.html#merlion.models.forecast.trees.LGBMForecasterConfig">LGBMForecasterConfig (class in merlion.models.forecast.trees)</a>
@@ -1265,6 +1267,10 @@ <h2 id="L">L</h2>
       <li><a href="merlion.models.html#merlion.models.factory.ModelFactory.load_bytes">load_bytes() (merlion.models.factory.ModelFactory class method)</a>
 </li>
       <li><a href="ts_datasets.anomaly.html#ts_datasets.anomaly.NAB.load_labels">load_labels() (ts_datasets.anomaly.NAB static method)</a>
+</li>
+      <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lof.LOF">LOF (class in merlion.models.anomaly.lof)</a>
+</li>
+      <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lof.LOFConfig">LOFConfig (class in merlion.models.anomaly.lof)</a>
 </li>
       <li><a href="merlion.models.forecast.html#merlion.models.forecast.smoother.DeltaEstimator.loss_err">loss_err() (merlion.models.forecast.smoother.DeltaEstimator method)</a>
 </li>
@@ -1532,6 +1538,13 @@ <h2 id="M">M</h2>
 
       <ul>
         <li><a href="merlion.models.anomaly.html#module-merlion.models.anomaly.isolation_forest">module</a>
+</li>
+      </ul></li>
+      <li>
+    merlion.models.anomaly.lof
+
+      <ul>
+        <li><a href="merlion.models.anomaly.html#module-merlion.models.anomaly.lof">module</a>
 </li>
       </ul></li>
       <li>
@@ -2141,6 +2154,8 @@ <h2 id="M">M</h2>
         <li><a href="merlion.models.anomaly.forecast_based.html#module-merlion.models.anomaly.forecast_based.sarima">merlion.models.anomaly.forecast_based.sarima</a>
 </li>
         <li><a href="merlion.models.anomaly.html#module-merlion.models.anomaly.isolation_forest">merlion.models.anomaly.isolation_forest</a>
+</li>
+        <li><a href="merlion.models.anomaly.html#module-merlion.models.anomaly.lof">merlion.models.anomaly.lof</a>
 </li>
         <li><a href="merlion.models.anomaly.html#module-merlion.models.anomaly.lstm_ed">merlion.models.anomaly.lstm_ed</a>
 </li>
@@ -2699,6 +2714,8 @@ <h2 id="R">R</h2>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetector.require_even_sampling">(merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetector property)</a>
 </li>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.isolation_forest.IsolationForest.require_even_sampling">(merlion.models.anomaly.isolation_forest.IsolationForest property)</a>
+</li>
+        <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lof.LOF.require_even_sampling">(merlion.models.anomaly.lof.LOF property)</a>
 </li>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lstm_ed.LSTMED.require_even_sampling">(merlion.models.anomaly.lstm_ed.LSTMED property)</a>
 </li>
@@ -2751,6 +2768,8 @@ <h2 id="R">R</h2>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetector.require_univariate">(merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetector property)</a>
 </li>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.isolation_forest.IsolationForest.require_univariate">(merlion.models.anomaly.isolation_forest.IsolationForest property)</a>
+</li>
+        <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lof.LOF.require_univariate">(merlion.models.anomaly.lof.LOF property)</a>
 </li>
         <li><a href="merlion.models.anomaly.html#merlion.models.anomaly.lstm_ed.LSTMED.require_univariate">(merlion.models.anomaly.lstm_ed.LSTMED property)</a>
 </li>
diff --git a/latest/merlion.models.anomaly.html b/latest/merlion.models.anomaly.html
index 0a93811c6..f065130b9 100644
--- a/latest/merlion.models.anomaly.html
+++ b/latest/merlion.models.anomaly.html
@@ -278,6 +278,16 @@
 </li>
 </ul>
 </li>
+<li class="toctree-l6"><a class="reference internal" href="#module-merlion.models.anomaly.lof">anomaly.lof</a><ul>
+<li class="toctree-l7"><a class="reference internal" href="#merlion.models.anomaly.lof.LOFConfig"><code class="docutils literal notranslate"><span class="pre">LOFConfig</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#merlion.models.anomaly.lof.LOF"><code class="docutils literal notranslate"><span class="pre">LOF</span></code></a><ul>
+<li class="toctree-l8"><a class="reference internal" href="#merlion.models.anomaly.lof.LOF.config_class"><code class="docutils literal notranslate"><span class="pre">LOF.config_class</span></code></a></li>
+<li class="toctree-l8"><a class="reference internal" href="#merlion.models.anomaly.lof.LOF.require_even_sampling"><code class="docutils literal notranslate"><span class="pre">LOF.require_even_sampling</span></code></a></li>
+<li class="toctree-l8"><a class="reference internal" href="#merlion.models.anomaly.lof.LOF.require_univariate"><code class="docutils literal notranslate"><span class="pre">LOF.require_univariate</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
 <li class="toctree-l6"><a class="reference internal" href="#module-merlion.models.anomaly.deep_point_anomaly_detector">anomaly.deep_point_anomaly_detector</a><ul>
 <li class="toctree-l7"><a class="reference internal" href="#merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetectorConfig"><code class="docutils literal notranslate"><span class="pre">DeepPointAnomalyDetectorConfig</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#merlion.models.anomaly.deep_point_anomaly_detector.DeepPointAnomalyDetector"><code class="docutils literal notranslate"><span class="pre">DeepPointAnomalyDetector</span></code></a><ul>
@@ -2074,6 +2084,128 @@ <h2>Multivariate models<a class="headerlink" href="#multivariate-models" title="
 
 </dd></dl>
 
+</div>
+<div class="section" id="module-merlion.models.anomaly.lof">
+<span id="anomaly-lof"></span><h3>anomaly.lof<a class="headerlink" href="#module-merlion.models.anomaly.lof" title="Permalink to this heading"></a></h3>
+<p>The classic LocalOutlierFactor model for anomaly detection.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="merlion.models.anomaly.lof.LOFConfig">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">merlion.models.anomaly.lof.</span></span><span class="sig-name descname"><span class="pre">LOFConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algorithm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leaf_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'minkowski'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contamination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">novelty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_score</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_calibrator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="merlion.transform.html#merlion.transform.base.TransformBase" title="merlion.transform.base.TransformBase"><span class="pre">TransformBase</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#merlion.models.anomaly.lof.LOFConfig" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#merlion.models.anomaly.base.DetectorConfig" title="merlion.models.anomaly.base.DetectorConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">DetectorConfig</span></code></a></p>
+<p>Configuration class for <cite>LocalOutlierFactor</cite>.</p>
+<dl>
+<dt>n_neighbors<span class="classifier">int, optional (default=20)</span></dt><dd><p>Number of neighbors to use by default for <cite>kneighbors</cite> queries.
+If n_neighbors is larger than the number of samples provided,
+all samples will be used.</p>
+</dd>
+<dt>algorithm<span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional</span></dt><dd><p>Algorithm used to compute the nearest neighbors:</p>
+<ul class="simple">
+<li><p>‘ball_tree’ will use BallTree</p></li>
+<li><p>‘kd_tree’ will use KDTree</p></li>
+<li><p>‘brute’ will use a brute-force search.</p></li>
+<li><p>‘auto’ will attempt to decide the most appropriate algorithm
+based on the values passed to <code class="xref py py-meth docutils literal notranslate"><span class="pre">fit()</span></code> method.</p></li>
+</ul>
+<p>Note: fitting on sparse input will override the setting of
+this parameter, using brute force.</p>
+</dd>
+<dt>leaf_size<span class="classifier">int, optional (default=30)</span></dt><dd><p>Leaf size passed to <cite>BallTree</cite> or <cite>KDTree</cite>. This can
+affect the speed of the construction and query, as well as the memory
+required to store the tree. The optimal value depends on the
+nature of the problem.</p>
+</dd>
+<dt>metric<span class="classifier">string or callable, default ‘minkowski’</span></dt><dd><p>metric used for the distance computation. Any metric from scikit-learn
+or scipy.spatial.distance can be used.</p>
+<p>If ‘precomputed’, the training input X is expected to be a distance
+matrix.</p>
+<p>If metric is a callable function, it is called on each
+pair of instances (rows) and the resulting value recorded. The callable
+should take two arrays as input and return one value indicating the
+distance between them. This works for Scipy’s metrics, but is less
+efficient than passing the metric name as a string.</p>
+<p>Valid values for metric are:</p>
+<ul class="simple">
+<li><p>from scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’,
+‘manhattan’]</p></li>
+<li><p>from scipy.spatial.distance: [‘braycurtis’, ‘canberra’, ‘chebyshev’,
+‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
+‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’,
+‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’,
+‘sqeuclidean’, ‘yule’]</p></li>
+</ul>
+<p>See the documentation for scipy.spatial.distance for details on these
+metrics:
+<a class="reference external" href="http://docs.scipy.org/doc/scipy/reference/spatial.distance.html">http://docs.scipy.org/doc/scipy/reference/spatial.distance.html</a></p>
+</dd>
+<dt>p<span class="classifier">integer, optional (default = 2)</span></dt><dd><p>Parameter for the Minkowski metric from
+sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
+equivalent to using manhattan_distance (l1), and euclidean_distance
+(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
+See <a class="reference external" href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances">http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances</a></p>
+</dd>
+<dt>metric_params<span class="classifier">dict, optional (default = None)</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
+</dd>
+<dt>contamination<span class="classifier">float in (0., 0.5), optional (default=0.1)</span></dt><dd><p>The amount of contamination of the data set, i.e. the proportion
+of outliers in the data set. When fitting this is used to define the
+threshold on the decision function.</p>
+</dd>
+<dt>n_jobs<span class="classifier">int, optional (default = 1)</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
+If <code class="docutils literal notranslate"><span class="pre">-1</span></code>, then the number of jobs is set to the number of CPU cores.
+Affects only kneighbors and kneighbors_graph methods.</p>
+</dd>
+<dt>novelty<span class="classifier">bool (default=False)</span></dt><dd><p>By default, LocalOutlierFactor is only meant to be used for outlier
+detection (novelty=False). Set novelty to True if you want to use
+LocalOutlierFactor for novelty detection. In this case be aware that
+that you should only use predict, decision_function and score_samples
+on new unseen data and not on the training set.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>max_score</strong> – maximum possible uncalibrated anomaly score</p></li>
+<li><p><strong>threshold</strong> – the rule to use for thresholding anomaly scores</p></li>
+<li><p><strong>enable_calibrator</strong> – whether to enable a calibrator which
+automatically transforms all raw anomaly scores to be z-scores
+(i.e. distributed as N(0, 1)).</p></li>
+<li><p><strong>enable_threshold</strong> – whether to enable the thresholding rule
+when post-processing anomaly scores</p></li>
+<li><p><strong>transform</strong> – Transformation to pre-process input time series.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="merlion.models.anomaly.lof.LOF">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">merlion.models.anomaly.lof.</span></span><span class="sig-name descname"><span class="pre">LOF</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#merlion.models.anomaly.lof.LOF" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#merlion.models.anomaly.base.DetectorBase" title="merlion.models.anomaly.base.DetectorBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">DetectorBase</span></code></a></p>
+<p>The classic LocalOutlierFactor sklearn implementation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>config</strong> (<a class="reference internal" href="#merlion.models.anomaly.lof.LOFConfig" title="merlion.models.anomaly.lof.LOFConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">LOFConfig</span></code></a>) – model configuration</p>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="merlion.models.anomaly.lof.LOF.config_class">
+<span class="sig-name descname"><span class="pre">config_class</span></span><a class="headerlink" href="#merlion.models.anomaly.lof.LOF.config_class" title="Permalink to this definition"></a></dt>
+<dd><p>alias of <a class="reference internal" href="#merlion.models.anomaly.lof.LOFConfig" title="merlion.models.anomaly.lof.LOFConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">LOFConfig</span></code></a></p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="merlion.models.anomaly.lof.LOF.require_even_sampling">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">require_even_sampling</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#merlion.models.anomaly.lof.LOF.require_even_sampling" title="Permalink to this definition"></a></dt>
+<dd><p>Whether the model assumes that training data is sampled at a fixed frequency</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="merlion.models.anomaly.lof.LOF.require_univariate">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">require_univariate</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#merlion.models.anomaly.lof.LOF.require_univariate" title="Permalink to this definition"></a></dt>
+<dd><p>Whether the model only works with univariate time series.</p>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="module-merlion.models.anomaly.deep_point_anomaly_detector">
 <span id="anomaly-deep-point-anomaly-detector"></span><h3>anomaly.deep_point_anomaly_detector<a class="headerlink" href="#module-merlion.models.anomaly.deep_point_anomaly_detector" title="Permalink to this heading"></a></h3>
diff --git a/latest/merlion.models.html b/latest/merlion.models.html
index 1ba695525..aca3f073b 100644
--- a/latest/merlion.models.html
+++ b/latest/merlion.models.html
@@ -374,6 +374,7 @@ <h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to thi
 <li class="toctree-l3"><a class="reference internal" href="merlion.models.anomaly.html#module-merlion.models.anomaly.vae">anomaly.vae</a></li>
 <li class="toctree-l3"><a class="reference internal" href="merlion.models.anomaly.html#module-merlion.models.anomaly.dagmm">anomaly.dagmm</a></li>
 <li class="toctree-l3"><a class="reference internal" href="merlion.models.anomaly.html#module-merlion.models.anomaly.lstm_ed">anomaly.lstm_ed</a></li>
+<li class="toctree-l3"><a class="reference internal" href="merlion.models.anomaly.html#module-merlion.models.anomaly.lof">anomaly.lof</a></li>
 <li class="toctree-l3"><a class="reference internal" href="merlion.models.anomaly.html#module-merlion.models.anomaly.deep_point_anomaly_detector">anomaly.deep_point_anomaly_detector</a></li>
 </ul>
 </li>
diff --git a/latest/objects.inv b/latest/objects.inv
index 4054827e30fe8c20f4bfd2c60286c8610d1cca24..30145e44ed0c47e0126de61ab9c2b6d96565b6f6 100644
GIT binary patch
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diff --git a/latest/py-modindex.html b/latest/py-modindex.html
index 5f0034af3..f53fe9ae8 100644
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        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
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+       <em></em></td></tr>
      <tr class="cg-1">
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index f54d98173..85302effa 100644
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"tutorials/forecast/0_ForecastIntro.ipynb", "tutorials/forecast/1_ForecastFeatures.ipynb", "tutorials/forecast/2_ForecastMultivariate.ipynb", "tutorials/forecast/3_ForecastExogenous.ipynb", "tutorials/forecast/4_ForecastNewModel.ipynb"], "titles": ["Merlion Architecture", "Welcome to Merlion\u2019s documentation!", "merlion: Time Series Intelligence", "merlion.dashboard package", "merlion.evaluate package", "merlion.models package", "anomaly", "anomaly.change_point", "anomaly.forecast_based", "automl", "ensemble", "forecast", "utils", "merlion.plot package", "merlion.post_process package", "merlion.spark package", "merlion.transform package", "merlion.utils package", "ts_datasets: Easy Data Loading", "ts_datasets.anomaly package", "ts_datasets.forecast package", "Tutorials &amp; Example Code", "Loading Custom Datasets", "Merlion\u2019s Data Format", "Tutorial for AutoSARIMA Forecasting Model", "Proof of Concept: Inverse Transforms for Forecasters", "A Gentle Introduction to Anomaly Detection in Merlion", "How to Use Anomaly Detectors in Merlion", "Multivariate Time Series Anomaly Detection", "Adding New Anomaly Detection Models", "A Gentle Introduction to Forecasting in Merlion", "How to Use Forecasters in Merlion", "Multivariate Time Series Forecasting", "Forecasting With Exogenous Regressors", "Adding a New Forecasting Model"], "terms": {"thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34], "document": [0, 2, 5, 6, 10, 11, 17], "i": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "intend": [0, 19], "develop": [0, 1, 15, 31], "It": [0, 1, 3, 4, 6, 11, 16, 19, 22, 27, 31, 34], "outlin": [0, 2, 18], "": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "kei": [0, 1, 2, 4, 5, 6, 9, 15, 16, 17, 18, 19, 25, 27, 31], "compon": [0, 1, 4, 8, 9, 11, 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[[1, null]], "Indices and tables": [[1, "indices-and-tables"]], "merlion: Time Series Intelligence": [[2, "merlion-time-series-intelligence"]], "merlion.dashboard package": [[3, "merlion-dashboard-package"]], "merlion.evaluate package": [[4, "merlion-evaluate-package"]], "merlion.evaluate.base": [[4, "module-merlion.evaluate.base"]], "merlion.evaluate.anomaly": [[4, "module-merlion.evaluate.anomaly"]], "merlion.evaluate.forecast": [[4, "module-merlion.evaluate.forecast"]], "merlion.models package": [[5, "merlion-models-package"]], "Subpackages": [[5, "subpackages"], [6, "subpackages"], [18, "subpackages"]], "defaults": [[5, "module-merlion.models.defaults"]], "factory": [[5, "module-merlion.models.factory"]], "base": [[5, "module-merlion.models.base"]], "deep_base": [[5, "module-merlion.models.deep_base"]], "layers": [[5, "module-merlion.models.layers"]], "anomaly": [[6, "module-merlion.models.anomaly"]], "Base classes": [[6, "base-classes"], [9, "base-classes"], [11, "base-classes"]], "anomaly.base": [[6, "module-merlion.models.anomaly.base"]], "Univariate models": [[6, "univariate-models"], [11, "univariate-models"]], "anomaly.dbl": [[6, "module-merlion.models.anomaly.dbl"]], "anomaly.windstats": [[6, "module-merlion.models.anomaly.windstats"]], "anomaly.spectral_residual": [[6, "module-merlion.models.anomaly.spectral_residual"]], "anomaly.stat_threshold": [[6, "module-merlion.models.anomaly.stat_threshold"]], "anomaly.zms": [[6, "module-merlion.models.anomaly.zms"]], "Multivariate models": [[6, "multivariate-models"], [11, "multivariate-models"]], "anomaly.isolation_forest": [[6, "module-merlion.models.anomaly.isolation_forest"]], "anomaly.random_cut_forest": [[6, "module-merlion.models.anomaly.random_cut_forest"]], "anomaly.autoencoder": [[6, "module-merlion.models.anomaly.autoencoder"]], "anomaly.vae": [[6, "module-merlion.models.anomaly.vae"]], "anomaly.dagmm": [[6, "module-merlion.models.anomaly.dagmm"]], "anomaly.lstm_ed": [[6, "module-merlion.models.anomaly.lstm_ed"]], "anomaly.deep_point_anomaly_detector": [[6, "module-merlion.models.anomaly.deep_point_anomaly_detector"]], "anomaly.change_point": [[7, "module-merlion.models.anomaly.change_point"]], "anomaly.change_point.bocpd": [[7, "module-merlion.models.anomaly.change_point.bocpd"]], "anomaly.forecast_based": [[8, "module-merlion.models.anomaly.forecast_based"]], "anomaly.forecast_based.base": [[8, "module-merlion.models.anomaly.forecast_based.base"]], "anomaly.forecast_based.arima": [[8, "module-merlion.models.anomaly.forecast_based.arima"]], "anomaly.forecast_based.sarima": [[8, "module-merlion.models.anomaly.forecast_based.sarima"]], "anomaly.forecast_based.ets": [[8, "module-merlion.models.anomaly.forecast_based.ets"]], "anomaly.forecast_based.prophet": [[8, "module-merlion.models.anomaly.forecast_based.prophet"]], "anomaly.forecast_based.mses": [[8, "module-merlion.models.anomaly.forecast_based.mses"]], "automl": [[9, "module-merlion.models.automl"]], "automl.base": [[9, "module-merlion.models.automl.base"]], "automl.autoets": [[9, "module-merlion.models.automl.autoets"]], "automl.autoprophet": [[9, "module-merlion.models.automl.autoprophet"]], "automl.autosarima": [[9, "module-merlion.models.automl.autosarima"]], "Utilities": [[9, "utilities"]], "automl.seasonality": [[9, "module-merlion.models.automl.seasonality"]], "automl.search": [[9, "module-merlion.models.automl.search"]], "ensemble": [[10, "module-merlion.models.ensemble"]], "ensemble.base": [[10, "module-merlion.models.ensemble.base"]], "ensemble.combine": [[10, "module-merlion.models.ensemble.combine"]], "ensemble.anomaly": [[10, "module-merlion.models.ensemble.anomaly"]], "ensemble.forecast": [[10, "module-merlion.models.ensemble.forecast"]], "forecast": [[11, "module-merlion.models.forecast"]], "forecast.base": [[11, "module-merlion.models.forecast.base"]], "forecast.deep_base": [[11, "module-merlion.models.forecast.deep_base"]], "forecast.sklearn_base": [[11, "module-merlion.models.forecast.sklearn_base"]], "forecast.arima": [[11, "module-merlion.models.forecast.arima"]], "forecast.sarima": [[11, "module-merlion.models.forecast.sarima"]], "forecast.ets": [[11, "module-merlion.models.forecast.ets"]], "forecast.prophet": [[11, "module-merlion.models.forecast.prophet"]], "forecast.smoother": [[11, "module-merlion.models.forecast.smoother"]], "forecast.vector_ar": [[11, "module-merlion.models.forecast.vector_ar"]], "forecast.trees": [[11, "module-merlion.models.forecast.trees"]], "forecast.deep_ar": [[11, "module-merlion.models.forecast.deep_ar"]], "forecast.autoformer": [[11, "module-merlion.models.forecast.autoformer"]], "forecast.etsformer": [[11, "module-merlion.models.forecast.etsformer"]], "forecast.informer": [[11, "module-merlion.models.forecast.informer"]], "forecast.transformer": [[11, "module-merlion.models.forecast.transformer"]], "utils": [[12, "module-merlion.models.utils"]], "utils.time_features": [[12, "module-merlion.models.utils.time_features"]], "utils.rolling_window_dataset": [[12, "module-merlion.models.utils.rolling_window_dataset"]], "utils.early_stopping": [[12, "module-merlion.models.utils.early_stopping"]], "utils.autosarima_utils": [[12, "module-merlion.models.utils.autosarima_utils"]], "merlion.plot package": [[13, "module-merlion.plot"]], "merlion.post_process package": [[14, "merlion-post-process-package"]], "merlion.post_process.base": [[14, "module-merlion.post_process.base"]], "merlion.post_process.factory": [[14, "module-merlion.post_process.factory"]], "merlion.post_process.sequence": [[14, "module-merlion.post_process.sequence"]], "merlion.post_process.calibrate": [[14, "merlion-post-process-calibrate"]], "merlion.post_process.threshold": [[14, "module-merlion.post_process.threshold"]], "merlion.spark package": [[15, "merlion-spark-package"]], "Setting up the spark-on-k8s-operator": [[15, "setting-up-the-spark-on-k8s-operator"]], "Specifying a Spark App": [[15, "specifying-a-spark-app"]], "API Documentation": [[15, "api-documentation"]], "merlion.spark.dataset": [[15, "module-merlion.spark.dataset"]], "merlion.spark.pandas_udf": [[15, "module-merlion.spark.pandas_udf"]], "merlion.transform package": [[16, "merlion-transform-package"]], "Base primitives": [[16, "base-primitives"]], "transform.factory": [[16, "module-merlion.transform.factory"]], "transform.base": [[16, "module-merlion.transform.base"]], "transform.sequence": [[16, "module-merlion.transform.sequence"]], "Resampling": [[16, "resampling"]], "transform.resample": [[16, "module-merlion.transform.resample"]], "transform.moving_average": [[16, "module-merlion.transform.moving_average"]], "Normalization": [[16, "normalization"]], "transform.normalize": [[16, "module-merlion.transform.normalize"]], "transform.bound": [[16, "module-merlion.transform.bound"]], "Miscellaneous": [[16, "miscellaneous"]], "transform.anomalize": [[16, "module-merlion.transform.anomalize"]], "merlion.utils package": [[17, "merlion-utils-package"]], "merlion.utils.time_series": [[17, "module-merlion.utils.time_series"]], "merlion.utils.resample": [[17, "module-merlion.utils.resample"]], "merlion.utils.data_io": [[17, "module-merlion.utils.data_io"]], "merlion.utils.hts": [[17, "module-merlion.utils.hts"]], "merlion.utils.ts_generator": [[17, "module-merlion.utils.ts_generator"]], "merlion.utils.conj_priors": [[17, "module-merlion.utils.conj_priors"]], "merlion.utils.istat": [[17, "module-merlion.utils.istat"]], "ts_datasets: Easy Data Loading": [[18, "ts-datasets-easy-data-loading"]], "datasets.base module": [[18, "module-ts_datasets.base"]], "ts_datasets.anomaly package": [[19, "module-ts_datasets.anomaly"]], "ts_datasets.forecast package": [[20, "module-ts_datasets.forecast"]], "Tutorials & Example Code": [[21, "tutorials-example-code"]], "Basics": [[21, "basics"]], "Anomaly Detection": [[21, "anomaly-detection"]], "Forecasting": [[21, "forecasting"]], "Advanced Features": [[21, "advanced-features"]], "Loading Custom Datasets": [[22, "Loading-Custom-Datasets"]], "Anomaly Detection Datasets": [[22, "Anomaly-Detection-Datasets"]], "General Purpose (Forecasting) Datasets": [[22, "General-Purpose-(Forecasting)-Datasets"]], "Broader Takeaways": [[22, "Broader-Takeaways"]], "Merlion\u2019s Data Format": [[23, "Merlion's-Data-Format"]], "UnivariateTimeSeries: The Basic Building Block": [[23, "UnivariateTimeSeries:-The-Basic-Building-Block"]], "TimeSeries: Merlion\u2019s Standard Data Class": [[23, "TimeSeries:-Merlion's-Standard-Data-Class"]], "Time Series Indexing & Alignment": [[23, "Time-Series-Indexing-&-Alignment"]], "TimeSeries: A Few Useful Features": [[23, "TimeSeries:-A-Few-Useful-Features"]], "Tutorial for AutoSARIMA Forecasting Model": [[24, "Tutorial-for-AutoSARIMA-Forecasting-Model"]], "Prepare dataset": [[24, "Prepare-dataset"]], "Train a full AutoSarima model with approximation (suggested, default)": [[24, "Train-a-full-AutoSarima-model-with-approximation-(suggested,-default)"]], "Train a full AutoSarima model without approximation (slower)": [[24, "Train-a-full-AutoSarima-model-without-approximation-(slower)"]], "Train a partial autosarima model": [[24, "Train-a-partial-autosarima-model"]], "Proof of Concept: Inverse Transforms for Forecasters": [[25, "Proof-of-Concept:-Inverse-Transforms-for-Forecasters"]], "A Gentle Introduction to Anomaly Detection in Merlion": [[26, "A-Gentle-Introduction-to-Anomaly-Detection-in-Merlion"]], "How to Use Anomaly Detectors in Merlion": [[27, "How-to-Use-Anomaly-Detectors-in-Merlion"]], "Model Initialization": [[27, "Model-Initialization"], [31, "Model-Initialization"]], "Model Training": [[27, "Model-Training"], [31, "Model-Training"]], "Model Inference": [[27, "Model-Inference"], [31, "Model-Inference"]], "Quantitative Evaluation": [[27, "Quantitative-Evaluation"], [29, "Quantitative-Evaluation"], [34, "Quantitative-Evaluation"]], "Model Visualization": [[27, "Model-Visualization"]], "Saving & Loading Models": [[27, "Saving-&-Loading-Models"], [31, "Saving-&-Loading-Models"]], "Simulating Live Model Deployment": [[27, "Simulating-Live-Model-Deployment"], [31, "Simulating-Live-Model-Deployment"]], "Multivariate Time Series Anomaly Detection": [[28, "Multivariate-Time-Series-Anomaly-Detection"]], "Model Initialization and Training": [[28, "Model-Initialization-and-Training"], [32, "Model-Initialization-and-Training"]], "Model Inference and Quantitative Evaluation": [[28, "Model-Inference-and-Quantitative-Evaluation"], [32, "Model-Inference-and-Quantitative-Evaluation"]], "Adding New Anomaly Detection Models": [[29, "Adding-New-Anomaly-Detection-Models"]], "Model Config Class": [[29, "Model-Config-Class"], [34, "Model-Config-Class"]], "Model Class": [[29, "Model-Class"], [34, "Model-Class"]], "Running the Model: A Simple Example": [[29, "Running-the-Model:-A-Simple-Example"], [34, "Running-the-Model:-A-Simple-Example"]], 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\ No newline at end of file
+Search.setIndex({"docnames": ["architecture", "index", "merlion", "merlion.dashboard", "merlion.evaluate", "merlion.models", "merlion.models.anomaly", "merlion.models.anomaly.change_point", "merlion.models.anomaly.forecast_based", "merlion.models.automl", "merlion.models.ensemble", "merlion.models.forecast", "merlion.models.utils", "merlion.plot", "merlion.post_process", "merlion.spark", "merlion.transform", "merlion.utils", "ts_datasets", "ts_datasets.anomaly", "ts_datasets.forecast", "tutorials", "tutorials/CustomDataset", "tutorials/TimeSeries", "tutorials/advanced/1_AutoSARIMA_forecasting_tutorial", "tutorials/advanced/2_ForecastInvertPOC", "tutorials/anomaly/0_AnomalyIntro", "tutorials/anomaly/1_AnomalyFeatures", "tutorials/anomaly/2_AnomalyMultivariate", "tutorials/anomaly/3_AnomalyNewModel", "tutorials/forecast/0_ForecastIntro", "tutorials/forecast/1_ForecastFeatures", "tutorials/forecast/2_ForecastMultivariate", "tutorials/forecast/3_ForecastExogenous", "tutorials/forecast/4_ForecastNewModel"], "filenames": ["architecture.rst", "index.rst", "merlion.rst", "merlion.dashboard.rst", "merlion.evaluate.rst", "merlion.models.rst", "merlion.models.anomaly.rst", "merlion.models.anomaly.change_point.rst", "merlion.models.anomaly.forecast_based.rst", "merlion.models.automl.rst", "merlion.models.ensemble.rst", "merlion.models.forecast.rst", "merlion.models.utils.rst", "merlion.plot.rst", "merlion.post_process.rst", "merlion.spark.rst", "merlion.transform.rst", "merlion.utils.rst", "ts_datasets.rst", "ts_datasets.anomaly.rst", "ts_datasets.forecast.rst", "tutorials.rst", "tutorials/CustomDataset.ipynb", "tutorials/TimeSeries.ipynb", "tutorials/advanced/1_AutoSARIMA_forecasting_tutorial.ipynb", "tutorials/advanced/2_ForecastInvertPOC.ipynb", "tutorials/anomaly/0_AnomalyIntro.ipynb", "tutorials/anomaly/1_AnomalyFeatures.ipynb", "tutorials/anomaly/2_AnomalyMultivariate.ipynb", "tutorials/anomaly/3_AnomalyNewModel.ipynb", "tutorials/forecast/0_ForecastIntro.ipynb", "tutorials/forecast/1_ForecastFeatures.ipynb", "tutorials/forecast/2_ForecastMultivariate.ipynb", "tutorials/forecast/3_ForecastExogenous.ipynb", "tutorials/forecast/4_ForecastNewModel.ipynb"], "titles": ["Merlion Architecture", "Welcome to Merlion\u2019s documentation!", "merlion: Time Series Intelligence", "merlion.dashboard package", "merlion.evaluate package", "merlion.models package", "anomaly", "anomaly.change_point", "anomaly.forecast_based", "automl", "ensemble", "forecast", "utils", "merlion.plot package", "merlion.post_process package", "merlion.spark package", "merlion.transform package", "merlion.utils package", "ts_datasets: Easy Data Loading", "ts_datasets.anomaly package", "ts_datasets.forecast package", "Tutorials &amp; Example Code", "Loading Custom Datasets", "Merlion\u2019s Data Format", "Tutorial for AutoSARIMA Forecasting Model", "Proof of Concept: Inverse Transforms for Forecasters", "A Gentle Introduction to Anomaly 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\ No newline at end of file