Add timeseries documentation to the master branch.

src/python/docs/docstrings/IIDChangePointDetector.txt

@@ -47,7 +47,6 @@
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude::
-        /../nimbusml/examples/IidSpikeChangePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/IidChangePointDetector_df.py
               :language: python
     """

src/python/docs/docstrings/IIDSpikeDetector.txt

@@ -9,7 +9,7 @@
         distribution. `Adaptive kernel density estimation
         <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_
         is used to model the distribution.
-        The `p-value <https://en.wikipedia.org/wiki/P-value`_> score
+        The `p-value <https://en.wikipedia.org/wiki/P-value>`_ score
         indicates the likelihood of the current observation according to
         the estimated distribution. The lower its value, the more likely the
         current point is an outlier.
@@ -29,6 +29,6 @@
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/IidSpikePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/IidSpikeDetector_df.py
               :language: python
     """

src/python/docs/docstrings/SsaChangePointDetector.txt

@@ -68,6 +68,6 @@
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/SsaChangePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaChangePointDetector_df.py
               :language: python
     """

src/python/docs/docstrings/SsaForecaster.txt

@@ -0,0 +1,31 @@
+    """
+    This transform forecasts using Singular Spectrum Analysis (SSA).
+
+    .. remarks::
+
+        This class implements the transform based on `Singular Spectrum Analysis
+        <https://en.wikipedia.org/wiki/Singular_spectrum_analysis>`_ (SSA).
+        SSA is a powerful framework for decomposing the time-series into trend,
+        seasonality and noise components as well as forecasting the future values
+        of the time-series. In principle, SSA performs spectral analysis on the
+        input time-series where each component in the spectrum corresponds to a
+        trend, seasonal or noise component in the time-series. For details of the
+        Singular Spectrum Analysis (SSA), refer to `this document
+        <http://arxiv.org/pdf/1206.6910.pdf>`_.
+
+    .. seealso::
+        :py:func:`IIDChangePointDetector
+        <nimbusml.preprocessing.timeseries.IIDChangePointDetector>`,
+        :py:func:`IIDSpikeDetector
+        <nimbusml.preprocessing.timeseries.IIDSpikeDetector>`,
+        :py:func:`SsaChangePointDetector
+        <nimbusml.preprocessing.timeseries.SsaChangePointDetector>`.
+        :py:func:`SsaSpikeDetector
+        <nimbusml.preprocessing.timeseries.SsaSpikeDetector>`.
+
+    .. index:: models, timeseries, transform
+
+    Example:
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaForecaster_df.py
+              :language: python
+    """

src/python/docs/docstrings/SsaSpikeDetector.txt

@@ -23,7 +23,7 @@
         kernel density estimation
         <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_.
 
-        The `p-value <https://en.wikipedia.org/wiki/P-value`_> score for the
+        The `p-value <https://en.wikipedia.org/wiki/P-value>`_ score for the
         current deviation is calculated based on the
         estimated distribution. The lower its value, the more likely the
         current point is an outlier.
@@ -60,6 +60,6 @@
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/SsaSpikeDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaSpikeDetector_df.py
               :language: python
     """

src/python/nimbusml.pyproj

@@ -999,6 +999,7 @@
     <Content Include="docs\docstrings\SkipFilter.txt" />
     <Content Include="docs\docstrings\SlidingWindow.txt" />
     <Content Include="docs\docstrings\SsaChangePointDetector.txt" />
+    <Content Include="docs\docstrings\SsaForecaster.txt" />
     <Content Include="docs\docstrings\SsaSpikeDetector.txt" />
     <Content Include="docs\docstrings\SsweEmbedding.txt" />
     <Content Include="docs\docstrings\SupervisedBinner.txt" />

src/python/nimbusml/internal/core/timeseries/iidchangepointdetector.py

@@ -68,8 +68,7 @@ class IidChangePointDetector(BasePipelineItem, DefaultSignature):
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude::
-        /../nimbusml/examples/IidSpikeChangePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/IidChangePointDetector_df.py
               :language: python
     """
 

src/python/nimbusml/internal/core/timeseries/iidspikedetector.py

@@ -28,7 +28,7 @@ class IidSpikeDetector(BasePipelineItem, DefaultSignature):
         distribution. `Adaptive kernel density estimation
         <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_
         is used to model the distribution.
-        The `p-value <https://en.wikipedia.org/wiki/P-value`_> score
+        The `p-value <https://en.wikipedia.org/wiki/P-value>`_ score
         indicates the likelihood of the current observation according to
         the estimated distribution. The lower its value, the more likely the
         current point is an outlier.
@@ -56,7 +56,7 @@ class IidSpikeDetector(BasePipelineItem, DefaultSignature):
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/IidSpikePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/IidSpikeDetector_df.py
               :language: python
     """
 

src/python/nimbusml/internal/core/timeseries/ssachangepointdetector.py

@@ -91,7 +91,7 @@ class SsaChangePointDetector(BasePipelineItem, DefaultSignature):
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/SsaChangePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaChangePointDetector_df.py
               :language: python
     """
 

src/python/nimbusml/internal/core/timeseries/ssaforecaster.py

@@ -18,8 +18,19 @@
 
 class SsaForecaster(BasePipelineItem, DefaultSignature):
     """
-    **Description**
-        This transform forecasts using Singular Spectrum Analysis (SSA).
+    This transform forecasts using Singular Spectrum Analysis (SSA).
+
+    .. remarks::
+
+        This class implements the transform based on `Singular Spectrum Analysis
+        <https://en.wikipedia.org/wiki/Singular_spectrum_analysis>`_ (SSA).
+        SSA is a powerful framework for decomposing the time-series into trend,
+        seasonality and noise components as well as forecasting the future values
+        of the time-series. In principle, SSA performs spectral analysis on the
+        input time-series where each component in the spectrum corresponds to a
+        trend, seasonal or noise component in the time-series. For details of the
+        Singular Spectrum Analysis (SSA), refer to `this document
+        <http://arxiv.org/pdf/1206.6910.pdf>`_.
 
     :param window_size: The length of the window on the series for building the
         trajectory matrix (parameter L).
@@ -69,6 +80,21 @@ class SsaForecaster(BasePipelineItem, DefaultSignature):
 
     :param params: Additional arguments sent to compute engine.
 
+    .. seealso::
+        :py:func:`IIDChangePointDetector
+        <nimbusml.preprocessing.timeseries.IIDChangePointDetector>`,
+        :py:func:`IIDSpikeDetector
+        <nimbusml.preprocessing.timeseries.IIDSpikeDetector>`,
+        :py:func:`SsaChangePointDetector
+        <nimbusml.preprocessing.timeseries.SsaChangePointDetector>`.
+        :py:func:`SsaSpikeDetector
+        <nimbusml.preprocessing.timeseries.SsaSpikeDetector>`.
+
+    .. index:: models, timeseries, transform
+
+    Example:
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaForecaster_df.py
+              :language: python
     """
 
     @trace

src/python/nimbusml/internal/core/timeseries/ssaspikedetector.py

@@ -42,7 +42,7 @@ class SsaSpikeDetector(BasePipelineItem, DefaultSignature):
         kernel density estimation
         <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_.
 
-        The `p-value <https://en.wikipedia.org/wiki/P-value`_> score for the
+        The `p-value <https://en.wikipedia.org/wiki/P-value>`_ score for the
         current deviation is calculated based on the
         estimated distribution. The lower its value, the more likely the
         current point is an outlier.
@@ -85,7 +85,7 @@ class SsaSpikeDetector(BasePipelineItem, DefaultSignature):
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/SsaSpikeDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaSpikeDetector_df.py
               :language: python
     """
 

src/python/nimbusml/timeseries/iidchangepointdetector.py

@@ -75,8 +75,7 @@ class IidChangePointDetector(
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude::
-        /../nimbusml/examples/IidSpikeChangePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/IidChangePointDetector_df.py
               :language: python
     """
 

src/python/nimbusml/timeseries/iidspikedetector.py

@@ -30,7 +30,7 @@ class IidSpikeDetector(core, BaseTransform, TransformerMixin):
         distribution. `Adaptive kernel density estimation
         <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_
         is used to model the distribution.
-        The `p-value <https://en.wikipedia.org/wiki/P-value`_> score
+        The `p-value <https://en.wikipedia.org/wiki/P-value>`_ score
         indicates the likelihood of the current observation according to
         the estimated distribution. The lower its value, the more likely the
         current point is an outlier.
@@ -60,7 +60,7 @@ class IidSpikeDetector(core, BaseTransform, TransformerMixin):
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/IidSpikePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/IidSpikeDetector_df.py
               :language: python
     """
 

src/python/nimbusml/timeseries/ssachangepointdetector.py

@@ -98,7 +98,7 @@ class SsaChangePointDetector(
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/SsaChangePointDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaChangePointDetector_df.py
               :language: python
     """
 

src/python/nimbusml/timeseries/ssaforecaster.py

@@ -19,8 +19,19 @@
 
 class SsaForecaster(core, BaseTransform, TransformerMixin):
     """
-    **Description**
-        This transform forecasts using Singular Spectrum Analysis (SSA).
+    This transform forecasts using Singular Spectrum Analysis (SSA).
+
+    .. remarks::
+
+        This class implements the transform based on `Singular Spectrum Analysis
+        <https://en.wikipedia.org/wiki/Singular_spectrum_analysis>`_ (SSA).
+        SSA is a powerful framework for decomposing the time-series into trend,
+        seasonality and noise components as well as forecasting the future values
+        of the time-series. In principle, SSA performs spectral analysis on the
+        input time-series where each component in the spectrum corresponds to a
+        trend, seasonal or noise component in the time-series. For details of the
+        Singular Spectrum Analysis (SSA), refer to `this document
+        <http://arxiv.org/pdf/1206.6910.pdf>`_.
 
     :param columns: see `Columns </nimbusml/concepts/columns>`_.
 
@@ -72,6 +83,21 @@ class SsaForecaster(core, BaseTransform, TransformerMixin):
 
     :param params: Additional arguments sent to compute engine.
 
+    .. seealso::
+        :py:func:`IIDChangePointDetector
+        <nimbusml.preprocessing.timeseries.IIDChangePointDetector>`,
+        :py:func:`IIDSpikeDetector
+        <nimbusml.preprocessing.timeseries.IIDSpikeDetector>`,
+        :py:func:`SsaChangePointDetector
+        <nimbusml.preprocessing.timeseries.SsaChangePointDetector>`.
+        :py:func:`SsaSpikeDetector
+        <nimbusml.preprocessing.timeseries.SsaSpikeDetector>`.
+
+    .. index:: models, timeseries, transform
+
+    Example:
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaForecaster_df.py
+              :language: python
     """
 
     @trace

src/python/nimbusml/timeseries/ssaspikedetector.py

@@ -44,7 +44,7 @@ class SsaSpikeDetector(core, BaseTransform, TransformerMixin):
         kernel density estimation
         <https://en.wikipedia.org/wiki/Variable_kernel_density_estimation>`_.
 
-        The `p-value <https://en.wikipedia.org/wiki/P-value`_> score for the
+        The `p-value <https://en.wikipedia.org/wiki/P-value>`_ score for the
         current deviation is calculated based on the
         estimated distribution. The lower its value, the more likely the
         current point is an outlier.
@@ -89,7 +89,7 @@ class SsaSpikeDetector(core, BaseTransform, TransformerMixin):
     .. index:: models, timeseries, transform
 
     Example:
-       .. literalinclude:: /../nimbusml/examples/SsaSpikeDetector.py
+       .. literalinclude:: /../nimbusml/examples/examples_from_dataframe/SsaSpikeDetector_df.py
               :language: python
     """