XML documentation for Time Series

docs/api-reference/io-time-series-change-point.md

@@ -0,0 +1,7 @@
+### Input and Output Columns
+There is only one input column and its type is <xref:System.Single>.
+This estimator adds the following output columns:
+
+| Output Column Name | Column Type | Description|
+| -- | -- | -- |
+| `Prediction` | 4-element vector of <xref:System.Double> | It sequentially contains alert level (non-zero value means a change point), score, p-value, and martingale value. |

docs/api-reference/io-time-series-spike.md

@@ -0,0 +1,7 @@
+### Input and Output Columns
+There is only one input column and its type is <xref:System.Single>.
+This estimator adds the following output columns:
+
+| Output Column Name | Column Type | Description|
+| -- | -- | -- |
+| `Prediction` | 3-element vector of <xref:System.Double> | It sequentially contains alert level (non-zero value means a change point), score, and p-value. |

docs/api-reference/time-series-iid.md

@@ -0,0 +1,4 @@
+### Training Algorithm Details
+This trainer assumes that data points collected in the time series are independently sampled from the same distribution (independent identically distributed).
+Thus, the value at the current timestamp can be viewed as the value at the next timestamp in expectation.
+If the observed value at timestamp $t-1$ is $p$, the predicted value at $t$ timestamp would be $p$ as well.

docs/api-reference/time-series-props.md

@@ -0,0 +1,7 @@
+### Estimator Characteristics
+|  |  |
+| -- | -- |
+| Machine learning task | Anomaly detection |
+| Is normalization required? | No |
+| Is caching required? | No |
+| Required NuGet in addition to Microsoft.ML | Microsoft.ML.TimeSeries |

docs/api-reference/time-series-scorer.md

@@ -0,0 +1,26 @@
+### Anomaly Scorer
+Once the raw score at a timestamp is computed, it is fed to the anomaly scorer component to calculate the final anomaly score at that timestamp.
+There are two statistics involved in this scorer, p-value and martingale score.
+
+#### Spike detection based on p-value
+The p-value score indicates the p-value of the current computed raw score according to a distribution of raw scores.
+Here, the distribution is estimated based on the most recent raw score values up to certain depth back in the history.
+More specifically, this distribution is estimated using [kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation)
+with the Gaussian [kernels](https://en.wikipedia.org/wiki/Kernel_(statistics)#In_non-parametric_statistics) of adaptive bandwidth.
+The p-value score is always in $[0, 1]$, and the lower its value, the more likely the current point is an outlier (also known as a spike).
+If the p-value score exceeds $1 - \frac{\text{confidence}}{100}$, the associated timestamp may get a non-zero alert value in spike detection, which means a spike point is detected.
+Note that $\text{confidence}$ is defined in the signatures of [DetectChangePointBySsa](xref:Microsoft.ML.TimeSeriesCatalog.DetectChangePointBySsa(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.ErrorFunction,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double))
+and [DetectIidChangePoint](xref:Microsoft.ML.TimeSeriesCatalog.DetectIidChangePoint(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double)).
+
+
+#### Change point detection based on martingale score
+The martingale score is an extra level of scoring that is built upon the p-value scores.
+The idea is based on the [Exchangeability Martingales](https://arxiv.org/pdf/1204.3251.pdf) that detect a change of distribution over a stream of i.i.d. values.
+In short, the value of the martingale score starts increasing significantly when a sequence of small p-values detected in a row; this indicates the change of the distribution of the underlying data generation process.
+Thus, the martingale score is used for change point detection.
+Given a sequence of most recently observed p-values, $p1, \dots, p_n$, the martingale score is computed as:? $s(p1, \dots, p_n) = \prod_{i=1}^n  \beta(p_i)$.
+There are two choices of $\beta$: $\beta(p) = e p^{\epsilon - 1}$ for $0 < \epsilon < 1$ or $\beta(p) = \int_{0}^1 \epsilon p^{\epsilon - 1} d\epsilon$.
+
+If the martingle score exceeds $s(q_1, \dots, q_n)$ where $q_i=1 - \frac{\text{confidence}}{100}$, the associated timestamp may get a non-zero alert value for change point detection.
+Note that $\text{confidence}$ is defined in the signatures of [DetectChangePointBySsa](xref:Microsoft.ML.TimeSeriesCatalog.DetectChangePointBySsa(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.ErrorFunction,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double)) or
+[DetectIidChangePoint](xref:Microsoft.ML.TimeSeriesCatalog.DetectIidChangePoint(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double)).

docs/api-reference/time-series-ssa.md

@@ -0,0 +1,5 @@
+### Training Algorithm Details
+This class implements the general anomaly detection transform based on [Singular Spectrum Analysis (SSA)](https://en.wikipedia.org/wiki/Singular_spectrum_analysis).
+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).

src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs

@@ -10,14 +10,14 @@ namespace Microsoft.ML
     public static class TimeSeriesCatalog
     {
         /// <summary>
-        /// Create a new instance of <see cref="IidChangePointEstimator"/> that detects a change of in an
-        /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables"> independent identically distributed (i.i.d.)</a> time series.
-        /// Detection is based on adaptive kernel density estimations and martingale scores.
+        /// Create <see cref="IidChangePointEstimator"/>, which predicts change points in an
+        /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables">independent identically distributed (i.i.d.)</a>
+        /// time series based on adaptive kernel density estimations and martingale scores.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
-        /// Column is a vector of type double and size 4. The vector contains Alert, Raw Score, P-Value and Martingale score as first four values.</param>
-        /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 4 elements: alert (non-zero value means a change point), raw score, p-Value and martingale score.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="confidence">The confidence for change point detection in the range [0, 100].</param>
         /// <param name="changeHistoryLength">The length of the sliding window on p-values for computing the martingale score.</param>
         /// <param name="martingale">The martingale used for scoring.</param>
@@ -34,13 +34,15 @@ public static IidChangePointEstimator DetectIidChangePoint(this TransformsCatalo
             => new IidChangePointEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, confidence, changeHistoryLength, inputColumnName, martingale, eps);
 
         /// <summary>
-        /// Create a new instance of <see cref="IidSpikeEstimator"/> that detects a spike in an
-        /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables">independent identically distributed (i.i.d.)</a> time series.
-        /// Detection is based on adaptive kernel density estimations and martingale scores.
+        /// Create <see cref="IidSpikeEstimator"/>, which predicts spikes in
+        /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables">independent identically distributed (i.i.d.)</a>
+        /// time series based on adaptive kernel density estimations and martingale scores.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/></param>.
-        /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 3 elements: alert (non-zero value means a spike), raw score, and p-value.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>.
+        /// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="confidence">The confidence for spike detection in the range [0, 100].</param>
         /// <param name="pvalueHistoryLength">The size of the sliding window for computing the p-value.</param>
         /// <param name="side">The argument that determines whether to detect positive or negative anomalies, or both.</param>
@@ -56,13 +58,14 @@ public static IidSpikeEstimator DetectIidSpike(this TransformsCatalog catalog, s
             => new IidSpikeEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, confidence, pvalueHistoryLength, inputColumnName, side);
 
         /// <summary>
-        /// Create a new instance of <see cref="SsaChangePointEstimator"/> for detecting a change in a time series signal
+        /// Create <see cref="SsaChangePointEstimator"/>, which predicts change points in time series
         /// using <a href="https://en.wikipedia.org/wiki/Singular_spectrum_analysis">Singular Spectrum Analysis (SSA)</a>.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
-        /// Column is a vector of type double and size 4. The vector contains Alert, Raw Score, P-Value and Martingale score as first four values.</param>
-        /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 4 elements: alert (non-zero value means a change point), raw score, p-Value and martingale score.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>.
+        /// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="confidence">The confidence for change point detection in the range [0, 100].</param>
         /// <param name="trainingWindowSize">The number of points from the beginning of the sequence used for training.</param>
         /// <param name="changeHistoryLength">The size of the sliding window for computing the p-value.</param>
@@ -94,17 +97,18 @@ public static SsaChangePointEstimator DetectChangePointBySsa(this TransformsCata
             });
 
         /// <summary>
-        /// Create a new instance of <see cref="SsaSpikeEstimator"/> for detecting a spike in a time series signal
+        /// Create <see cref="SsaSpikeEstimator"/>, which predicts spikes in time series
         /// using <a href="https://en.wikipedia.org/wiki/Singular_spectrum_analysis">Singular Spectrum Analysis (SSA)</a>.
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
+        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+        /// The column data is a vector of <see cref="System.Double"/>. The vector contains 3 elements: alert (non-zero value means a spike), raw score, and p-value.</param>
+        /// <param name="inputColumnName">Name of column to transform. The column data must be <see cref="System.Single"/>.
+        /// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="confidence">The confidence for spike detection in the range [0, 100].</param>
         /// <param name="pvalueHistoryLength">The size of the sliding window for computing the p-value.</param>
         /// <param name="trainingWindowSize">The number of points from the beginning of the sequence used for training.</param>
         /// <param name="seasonalityWindowSize">An upper bound on the largest relevant seasonality in the input time-series.</param>
-        /// The vector contains Alert, Raw Score, P-Value as first three values.</param>
         /// <param name="side">The argument that determines whether to detect positive or negative anomalies, or both.</param>
         /// <param name="errorFunction">The function used to compute the error between the expected and the observed value.</param>
         /// <example>

src/Microsoft.ML.TimeSeries/IidAnomalyDetectionBase.cs

@@ -23,7 +23,7 @@ public class IidAnomalyDetectionBaseWrapper : IStatefulTransformer, ICanSaveMode
         bool ITransformer.IsRowToRowMapper => ((ITransformer)InternalTransform).IsRowToRowMapper;
 
         /// <summary>
-        /// Creates a clone of the transfomer. Used for taking the snapshot of the state.
+        /// Create a clone of the transformer. Used for taking the snapshot of the state.
         /// </summary>
         /// <returns></returns>
         IStatefulTransformer IStatefulTransformer.Clone() => InternalTransform.Clone();

src/Microsoft.ML.TimeSeries/IidChangePointDetector.cs

@@ -191,10 +191,25 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat
     }
 
     /// <summary>
-    /// The <see cref="IEstimator{ITransformer}"/> for detecting a signal change on an
-    /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables"> independent identically distributed (i.i.d.)</a> time series.
-    /// Detection is based on adaptive kernel density estimation and martingales.
+    /// The <see cref="IEstimator{TTransformer}"/> to detect a signal change on an
+    /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables"> independent identically distributed (i.i.d.)</a>
+    /// time series based on adaptive kernel density estimation and martingales.
     /// </summary>
+    /// <remarks>
+    /// <format type="text/markdown"><![CDATA[
+    /// To create this estimator, use [DetectIidChangePoint](xref:Microsoft.ML.TimeSeriesCatalog.DetectIidChangePoint(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double)).
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/io-time-series-change-point.md)]
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/time-series-props.md)]
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/time-series-iid.md)]
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/time-series-scorer.md)]
+    /// ]]>
+    /// </format>
+    /// </remarks>
+    /// <seealso cref="Microsoft.ML.TimeSeriesCatalog.DetectIidChangePoint(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.MartingaleType,System.Double)" />
     public sealed class IidChangePointEstimator : TrivialEstimator<IidChangePointDetector>
     {
         /// <summary>

src/Microsoft.ML.TimeSeries/IidSpikeDetector.cs

@@ -171,10 +171,25 @@ private static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, Dat
     }
 
     /// <summary>
-    /// The <see cref="IEstimator{ITransformer}"/> for detecting a signal spike on an
-    /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables"> independent identically distributed (i.i.d.)</a> time series.
-    /// Detection is based on adaptive kernel density estimation.
+    /// The <see cref="IEstimator{TTransformer}"/> to detect a signal spike on an
+    /// <a href="https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables"> independent identically distributed (i.i.d.)</a>
+    /// time series based on adaptive kernel density estimation.
     /// </summary>
+    /// <remarks>
+    /// <format type="text/markdown"><![CDATA[
+    /// To create this estimator, use [DetectIidSpike](xref:Microsoft.ML.TimeSeriesCatalog.DetectIidSpike(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.AnomalySide)).
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/io-time-series-spike.md)]
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/time-series-props.md)]
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/time-series-iid.md)]
+    ///
+    /// [!include[io](~/../docs/samples/docs/api-reference/time-series-scorer.md)]
+    /// ]]>
+    /// </format>
+    /// </remarks>
+    /// <seealso cref="Microsoft.ML.TimeSeriesCatalog.DetectIidSpike(Microsoft.ML.TransformsCatalog,System.String,System.String,System.Int32,System.Int32,Microsoft.ML.Transforms.TimeSeries.AnomalySide)" />
     public sealed class IidSpikeEstimator : TrivialEstimator<IidSpikeDetector>
     {
         /// <summary>

src/Microsoft.ML.TimeSeries/SsaAnomalyDetectionBase.cs

@@ -92,7 +92,7 @@ public class SsaAnomalyDetectionBaseWrapper : IStatefulTransformer, ICanSaveMode
         bool ITransformer.IsRowToRowMapper => ((ITransformer)InternalTransform).IsRowToRowMapper;
 
         /// <summary>
-        /// Creates a clone of the transfomer. Used for taking the snapshot of the state.
+        /// Creates a clone of the transformer. Used for taking the snapshot of the state.
         /// </summary>
         /// <returns></returns>
         IStatefulTransformer IStatefulTransformer.Clone() => InternalTransform.Clone();
@@ -340,7 +340,7 @@ private protected override void InitializeAnomalyDetector()
 
                 private protected override double ComputeRawAnomalyScore(ref Single input, FixedSizeQueue<Single> windowedBuffer, long iteration)
                 {
-                    // Get the prediction for the next point opn the series
+                    // Get the prediction for the next point in the series
                     Single expectedValue = 0;
                     _model.PredictNext(ref expectedValue);