TimeSeriesCatalog.DetectChangePointBySsa Método
Definição
Importante
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Sobrecargas
| DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double) |
Criar SsaChangePointEstimator, que prevê pontos de alteração na série temporal usando a Singular Spectrum Analysis (SSA). |
| DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double) |
Obsoleto.
Criar SsaChangePointEstimator, que prevê pontos de alteração na série temporal usando a Singular Spectrum Analysis (SSA). |
DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
Criar SsaChangePointEstimator, que prevê pontos de alteração na série temporal usando a 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 SsaChangePointEstimatorParâmetros
- catalog
- TransformsCatalog
O catálogo da transformação.
- outputColumnName
- String
Nome da coluna resultante da transformação de inputColumnName.
Os dados da coluna são um vetor de Double. O vetor contém 4 elementos: alerta (valor diferente de zero significa um ponto de alteração), pontuação bruta, pontuação p-Value e martingale.
- inputColumnName
- String
Nome da coluna a ser transformada. Os dados da coluna devem ser Single.
Se definido como null, o valor do outputColumnName será usado como origem.
- confidence
- Double
A confiança para a detecção de ponto de alteração no intervalo [0, 100].
- changeHistoryLength
- Int32
O tamanho da janela deslizante para calcular o p-value.
- trainingWindowSize
- Int32
O número de pontos desde o início da sequência usada para treinamento.
- seasonalityWindowSize
- Int32
Um limite superior na maior sazonalidade relevante na série temporal de entrada.
- errorFunction
- ErrorFunction
A função usada para calcular o erro entre o valor esperado e o observado.
- martingale
- MartingaleType
O martingale usado para marcar.
- eps
- Double
O parâmetro epsilon para o martingale do Power.
Retornos
Exemplos
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;
}
}
}
}
Aplica-se a
DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
Cuidado
This API method is deprecated, please use the overload with confidence parameter of type double.
Criar SsaChangePointEstimator, que prevê pontos de alteração na série temporal usando a 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 SsaChangePointEstimatorParâmetros
- catalog
- TransformsCatalog
O catálogo da transformação.
- outputColumnName
- String
Nome da coluna resultante da transformação de inputColumnName.
Os dados da coluna são um vetor de Double. O vetor contém 4 elementos: alerta (valor diferente de zero significa um ponto de alteração), pontuação bruta, pontuação p-Value e martingale.
- inputColumnName
- String
Nome da coluna a ser transformada. Os dados da coluna devem ser Single.
Se definido como null, o valor do outputColumnName será usado como origem.
- confidence
- Int32
A confiança para a detecção de ponto de alteração no intervalo [0, 100].
- changeHistoryLength
- Int32
O tamanho da janela deslizante para calcular o p-value.
- trainingWindowSize
- Int32
O número de pontos desde o início da sequência usada para treinamento.
- seasonalityWindowSize
- Int32
Um limite superior na maior sazonalidade relevante na série temporal de entrada.
- errorFunction
- ErrorFunction
A função usada para calcular o erro entre o valor esperado e o observado.
- martingale
- MartingaleType
O martingale usado para marcar.
- eps
- Double
O parâmetro epsilon para o martingale do Power.
Retornos
- Atributos
Exemplos
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;
}
}
}
}