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TimeSeriesCatalog.DetectChangePointBySsa Metoda

Definice

Přetížení

DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)

Vytvořte SsaChangePointEstimator, který predikuje body změn v časových řadách pomocí jednotné analýzy spektra (SSA).

DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)
Zastaralé.

Vytvořte SsaChangePointEstimator, který predikuje body změn v časových řadách pomocí jednotné analýzy spektra (SSA).

DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)

Vytvořte SsaChangePointEstimator, který predikuje body změn v časových řadách pomocí jednotné analýzy spektra (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 SsaChangePointEstimator

Parametry

catalog
TransformsCatalog

Katalog transformace.

outputColumnName
String

Název sloupce, který je výsledkem transformace inputColumnName. Data ve sloupci jsou vektorem Double. Vektor obsahuje 4 prvky: výstraha (nenulová hodnota znamená bod změny), nezpracované skóre, p-Hodnota a martingale skóre.

inputColumnName
String

Název sloupce, který se má transformovat. Data sloupce musí být Single. Pokud je nastavená hodnota null, použije se jako zdroj hodnota outputColumnName .

confidence
Double

Spolehlivost detekce bodu změny v rozsahu [0, 100].

changeHistoryLength
Int32

Velikost posuvného okna pro výpočet p-hodnoty.

trainingWindowSize
Int32

Počet bodů od začátku sekvence použité pro trénování.

seasonalityWindowSize
Int32

Horní mez největší relevantní sezónnosti ve vstupní časové řadě.

errorFunction
ErrorFunction

Funkce použitá k výpočtu chyby mezi očekávanou a pozorovanou hodnotou.

martingale
MartingaleType

Martingale se použil k bodování.

eps
Double

Epsilon parametr power martingale.

Návraty

Příklady

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;
            }
        }
    }
}

Platí pro

DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)

Upozornění

This API method is deprecated, please use the overload with confidence parameter of type double.

Vytvořte SsaChangePointEstimator, který predikuje body změn v časových řadách pomocí jednotné analýzy spektra (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.SsaChangePointEstimator
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.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 SsaChangePointEstimator

Parametry

catalog
TransformsCatalog

Katalog transformace.

outputColumnName
String

Název sloupce, který je výsledkem transformace inputColumnName. Data ve sloupci jsou vektorem Double. Vektor obsahuje 4 prvky: výstraha (nenulová hodnota znamená bod změny), nezpracované skóre, p-Hodnota a martingale skóre.

inputColumnName
String

Název sloupce, který se má transformovat. Data sloupce musí být Single. Pokud je nastavená hodnota null, použije se jako zdroj hodnota outputColumnName .

confidence
Int32

Spolehlivost detekce bodu změny v rozsahu [0, 100].

changeHistoryLength
Int32

Velikost posuvného okna pro výpočet p-hodnoty.

trainingWindowSize
Int32

Počet bodů od začátku sekvence použité pro trénování.

seasonalityWindowSize
Int32

Horní mez největší relevantní sezónnosti ve vstupní časové řadě.

errorFunction
ErrorFunction

Funkce použitá k výpočtu chyby mezi očekávanou a pozorovanou hodnotou.

martingale
MartingaleType

Martingale se použil k bodování.

eps
Double

Epsilon parametr power martingale.

Návraty

Atributy

Příklady

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;
            }
        }
    }
}

Platí pro