TimeSeriesCatalog.DetectChangePointBySsa Method
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
Overloads
| DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double) |
Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA). |
| DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double) |
Obsolete.
Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA). |
DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA).
public static Microsoft.ML.Transforms.TimeSeries.SsaChangePointEstimator DetectChangePointBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int changeHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);static member DetectChangePointBySsa : Microsoft.ML.TransformsCatalog * string * string * double * int * int * int * Microsoft.ML.Transforms.TimeSeries.ErrorFunction * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.SsaChangePointEstimator<Extension()>
Public Function DetectChangePointBySsa (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Double, changeHistoryLength As Integer, trainingWindowSize As Integer, seasonalityWindowSize As Integer, Optional errorFunction As ErrorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference, Optional martingale As MartingaleType = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, Optional eps As Double = 0.1) As SsaChangePointEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnName
- String
Name of the column resulting from the transformation of inputColumnName.
The column data is a vector of Double. The vector contains 4 elements: alert (non-zero value means a change point), raw score, p-Value and martingale score.
- inputColumnName
- String
Name of column to transform. The column data must be Single.
If set to null, the value of the outputColumnName will be used as source.
- confidence
- Double
The confidence for change point detection in the range [0, 100].
- changeHistoryLength
- Int32
The size of the sliding window for computing the p-value.
- trainingWindowSize
- Int32
The number of points from the beginning of the sequence used for training.
- seasonalityWindowSize
- Int32
An upper bound on the largest relevant seasonality in the input time-series.
- errorFunction
- ErrorFunction
The function used to compute the error between the expected and the observed value.
- martingale
- MartingaleType
The martingale used for scoring.
- eps
- Double
The epsilon parameter for the Power martingale.
Returns
Examples
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
{
// This example creates a time series (list of Data with the i-th element
// corresponding to the i-th time slot). The estimator is applied then to
// identify points where data distribution changed. This estimator can
// account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var ml = new MLContext();
// Generate sample series data with a recurring pattern and then a
// change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);
// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(
outputColumnName, inputColumnName, 95.0d, 8, TrainingSize,
SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine(outputColumnName + " column obtained " +
"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}
private static void PrintPrediction(float value, ChangePointPrediction
prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value,
prediction.Prediction[0], prediction.Prediction[1],
prediction.Prediction[2], prediction.Prediction[3]);
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}
class TimeSeriesData
{
public float Value;
public TimeSeriesData(float value)
{
Value = value;
}
}
}
}
Applies to
DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
Caution
This API method is deprecated, please use the overload with confidence parameter of type double.
Create SsaChangePointEstimator, which predicts change points in time series using Singular Spectrum Analysis (SSA).
[System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
public static Microsoft.ML.Transforms.TimeSeries.SsaChangePointEstimator DetectChangePointBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int changeHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);public static Microsoft.ML.Transforms.TimeSeries.SsaChangePointEstimator DetectChangePointBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int changeHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);[<System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")>]
static member DetectChangePointBySsa : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * Microsoft.ML.Transforms.TimeSeries.ErrorFunction * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.SsaChangePointEstimatorstatic member DetectChangePointBySsa : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * Microsoft.ML.Transforms.TimeSeries.ErrorFunction * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.SsaChangePointEstimator<Extension()>
Public Function DetectChangePointBySsa (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Integer, changeHistoryLength As Integer, trainingWindowSize As Integer, seasonalityWindowSize As Integer, Optional errorFunction As ErrorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference, Optional martingale As MartingaleType = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, Optional eps As Double = 0.1) As SsaChangePointEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnName
- String
Name of the column resulting from the transformation of inputColumnName.
The column data is a vector of Double. The vector contains 4 elements: alert (non-zero value means a change point), raw score, p-Value and martingale score.
- inputColumnName
- String
Name of column to transform. The column data must be Single.
If set to null, the value of the outputColumnName will be used as source.
- confidence
- Int32
The confidence for change point detection in the range [0, 100].
- changeHistoryLength
- Int32
The size of the sliding window for computing the p-value.
- trainingWindowSize
- Int32
The number of points from the beginning of the sequence used for training.
- seasonalityWindowSize
- Int32
An upper bound on the largest relevant seasonality in the input time-series.
- errorFunction
- ErrorFunction
The function used to compute the error between the expected and the observed value.
- martingale
- MartingaleType
The martingale used for scoring.
- eps
- Double
The epsilon parameter for the Power martingale.
Returns
- Attributes
Examples
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectChangePointBySsaBatchPrediction
{
// This example creates a time series (list of Data with the i-th element
// corresponding to the i-th time slot). The estimator is applied then to
// identify points where data distribution changed. This estimator can
// account for temporal seasonality in the data.
public static void Example()
{
// Create a new ML context, for ML.NET operations. It can be used for
// exception tracking and logging, as well as the source of randomness.
var ml = new MLContext();
// Generate sample series data with a recurring pattern and then a
// change in trend
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List<TimeSeriesData>()
{
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
new TimeSeriesData(0),
new TimeSeriesData(1),
new TimeSeriesData(2),
new TimeSeriesData(3),
new TimeSeriesData(4),
//This is a change point
new TimeSeriesData(0),
new TimeSeriesData(100),
new TimeSeriesData(200),
new TimeSeriesData(300),
new TimeSeriesData(400),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup estimator arguments
var inputColumnName = nameof(TimeSeriesData.Value);
var outputColumnName = nameof(ChangePointPrediction.Prediction);
// The transformed data.
var transformedData = ml.Transforms.DetectChangePointBySsa(
outputColumnName, inputColumnName, 95.0d, 8, TrainingSize,
SeasonalitySize + 1).Fit(dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine(outputColumnName + " column obtained " +
"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 0 0 -2.53 0.50 0.00
// 1 0 -0.01 0.01 0.00
// 2 0 0.76 0.14 0.00
// 3 0 0.69 0.28 0.00
// 4 0 1.44 0.18 0.00
// 0 0 -1.84 0.17 0.00
// 1 0 0.22 0.44 0.00
// 2 0 0.20 0.45 0.00
// 3 0 0.16 0.47 0.00
// 4 0 1.33 0.18 0.00
// 0 0 -1.79 0.07 0.00
// 1 0 0.16 0.50 0.00
// 2 0 0.09 0.50 0.00
// 3 0 0.08 0.45 0.00
// 4 0 1.31 0.12 0.00
// 0 0 -1.79 0.07 0.00
// 100 1 99.16 0.00 4031.94 <-- alert is on, predicted changepoint
// 200 0 185.23 0.00 731260.87
// 300 0 270.40 0.01 3578470.47
// 400 0 357.11 0.03 45298370.86
}
private static void PrintPrediction(float value, ChangePointPrediction
prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value,
prediction.Prediction[0], prediction.Prediction[1],
prediction.Prediction[2], prediction.Prediction[3]);
class ChangePointPrediction
{
[VectorType(4)]
public double[] Prediction { get; set; }
}
class TimeSeriesData
{
public float Value;
public TimeSeriesData(float value)
{
Value = value;
}
}
}
}