TimeSeriesCatalog.DetectChangePointBySsa Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.TimeSeries.dll

Paket:

Microsoft.ML.TimeSeries v4.0.0-preview.24522.7

Overload

DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)	Buat SsaChangePointEstimator, yang memprediksi titik perubahan dalam rangkaian waktu menggunakan Analisis Spektrum Tunggal (SSA).
DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)	Kedaluwarsa. Buat SsaChangePointEstimator, yang memprediksi titik perubahan dalam rangkaian waktu menggunakan Analisis Spektrum Tunggal (SSA).

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

Sumber:

ExtensionsCatalog.cs

Buat SsaChangePointEstimator, yang memprediksi titik perubahan dalam rangkaian waktu menggunakan Analisis Spektrum Tunggal (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

Parameter

catalog

TransformsCatalog

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Data kolom adalah vektor dari Double. Vektor berisi 4 elemen: pemberitahuan (nilai bukan nol berarti titik perubahan), skor mentah, p-Value, dan skor martingale.

inputColumnName

String

Nama kolom yang akan diubah. Data kolom harus Single. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber.

confidence

Double

Keyakinan untuk deteksi titik perubahan dalam rentang [0, 100].

changeHistoryLength

Int32

Ukuran jendela geser untuk menghitung nilai p.

trainingWindowSize

Int32

Jumlah titik dari awal urutan yang digunakan untuk pelatihan.

seasonalityWindowSize

Int32

Batas atas pada musiman relevan terbesar dalam rangkaian waktu input.

errorFunction

ErrorFunction

Fungsi yang digunakan untuk menghitung kesalahan antara nilai yang diharapkan dan yang diamati.

martingale

MartingaleType

Martingale digunakan untuk penilaian.

eps

Double

Parameter epsilon untuk Martingale Power.

Mengembalikan

SsaChangePointEstimator

Contoh

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;
            }
        }
    }
}

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

Sumber:

ExtensionsCatalog.cs

Perhatian

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

Buat SsaChangePointEstimator, yang memprediksi titik perubahan dalam rangkaian waktu menggunakan Analisis Spektrum Tunggal (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);

[<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

<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

Parameter

catalog

TransformsCatalog

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Data kolom adalah vektor dari Double. Vektor berisi 4 elemen: pemberitahuan (nilai bukan nol berarti titik perubahan), skor mentah, p-Value, dan skor martingale.

inputColumnName

String

Nama kolom yang akan diubah. Data kolom harus Single. Jika diatur ke null, nilai outputColumnName akan digunakan sebagai sumber.

confidence

Int32

Keyakinan untuk deteksi titik perubahan dalam rentang [0, 100].

changeHistoryLength

Int32

Ukuran jendela geser untuk menghitung nilai p.

trainingWindowSize

Int32

Jumlah titik dari awal urutan yang digunakan untuk pelatihan.

seasonalityWindowSize

Int32

Batas atas pada musiman relevan terbesar dalam rangkaian waktu input.

errorFunction

ErrorFunction

Fungsi yang digunakan untuk menghitung kesalahan antara nilai yang diharapkan dan yang diamati.

martingale

MartingaleType

Martingale digunakan untuk penilaian.

eps

Double

Parameter epsilon untuk Martingale Power.

Mengembalikan

SsaChangePointEstimator

Atribut

ObsoleteAttribute

Contoh

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;
            }
        }
    }
}