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TimeSeriesCatalog.DetectSpikeBySsa Metoda

  • Reference

Definice

Obor názvů:
Microsoft.ML
Sestavení:
Microsoft.ML.TimeSeries.dll
Balíček:
Microsoft.ML.TimeSeries v3.0.1
Balíček:
Microsoft.ML.TimeSeries v1.5.5
Balíček:
Microsoft.ML.TimeSeries v1.6.0
Balíček:
Microsoft.ML.TimeSeries v1.7.0
Balíček:
Microsoft.ML.TimeSeries v2.0.0
Balíček:
Microsoft.ML.TimeSeries v1.2.0
Balíček:
Microsoft.ML.TimeSeries v1.3.1
Balíček:
Microsoft.ML.TimeSeries v1.4.0

Přetížení

DetectSpikeBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, AnomalySide, ErrorFunction)

Vytvořte SsaSpikeEstimator, což predikuje špičky v časových řadách pomocí analýzy jednotného spektra (SSA).
DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction)
Zastaralé.

Vytvořte SsaSpikeEstimator, což predikuje špičky v časových řadách pomocí analýzy jednotného spektra (SSA).

DetectSpikeBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, AnomalySide, ErrorFunction)

Vytvořte SsaSpikeEstimator, což predikuje špičky v časových řadách pomocí analýzy jednotného spektra (SSA).

public static Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator DetectSpikeBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int pvalueHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference);
static member DetectSpikeBySsa : Microsoft.ML.TransformsCatalog * string * string * double * int * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide * Microsoft.ML.Transforms.TimeSeries.ErrorFunction -> Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator
<Extension()>
Public Function DetectSpikeBySsa (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Double, pvalueHistoryLength As Integer, trainingWindowSize As Integer, seasonalityWindowSize As Integer, Optional side As AnomalySide = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Optional errorFunction As ErrorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference) As SsaSpikeEstimator

Parametry

catalog
TransformsCatalog

Katalog transformace.

outputColumnName
String

Název sloupce, který je výsledkem transformace inputColumnName. Data ve sloupci jsou vektorem Double. Vektor obsahuje 3 prvky: výstraha (nenulová hodnota znamená špičku), nezpracované skóre a p-hodnota.

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 špiček v rozsahu [0, 100].

pvalueHistoryLength
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.

side
AnomalySide

Argument, který určuje, jestli se mají detekovat pozitivní nebo záporné anomálie, nebo obojí.

errorFunction
ErrorFunction

Funkce použitá k výpočtu chyby mezi očekávanou a pozorovanou hodnotou.

Návraty

SsaSpikeEstimator

Příklady

using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public static class DetectSpikeBySsaBatchPrediction
    {
        // 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 spiking points in the series. 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 a spike
            // within the pattern
            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 spike.
                new TimeSeriesData(100),

                new TimeSeriesData(0),
                new TimeSeriesData(1),
                new TimeSeriesData(2),
                new TimeSeriesData(3),
                new TimeSeriesData(4),
            };

            // Convert data to IDataView.
            var dataView = ml.Data.LoadFromEnumerable(data);

            // Setup estimator arguments
            var inputColumnName = nameof(TimeSeriesData.Value);
            var outputColumnName = nameof(SsaSpikePrediction.Prediction);

            // The transformed data.
            var transformedData = ml.Transforms.DetectSpikeBySsa(outputColumnName,
                inputColumnName, 95.0d, 8, TrainingSize, SeasonalitySize + 1).Fit(
                dataView).Transform(dataView);

            // Getting the data of the newly created column as an IEnumerable of
            // SsaSpikePrediction.
            var predictionColumn = ml.Data.CreateEnumerable<SsaSpikePrediction>(
                transformedData, reuseRowObject: false);

            Console.WriteLine($"{outputColumnName} column obtained " +
                $"post-transformation.");

            Console.WriteLine("Data\tAlert\tScore\tP-Value");
            int k = 0;
            foreach (var prediction in predictionColumn)
                PrintPrediction(data[k++].Value, prediction);

            // Prediction column obtained post-transformation.
            // Data    Alert   Score   P-Value
            // 0       0      -2.53    0.50
            // 1       0      -0.01    0.01
            // 2       0       0.76    0.14
            // 3       0       0.69    0.28
            // 4       0       1.44    0.18
            // 0       0      -1.84    0.17
            // 1       0       0.22    0.44
            // 2       0       0.20    0.45
            // 3       0       0.16    0.47
            // 4       0       1.33    0.18
            // 0       0      -1.79    0.07
            // 1       0       0.16    0.50
            // 2       0       0.09    0.50
            // 3       0       0.08    0.45
            // 4       0       1.31    0.12
            // 100     1      98.21    0.00   <-- alert is on, predicted spike
            // 0       0     -13.83    0.29
            // 1       0      -1.74    0.44
            // 2       0      -0.47    0.46
            // 3       0     -16.50    0.29
            // 4       0     -29.82    0.21
        }

        private static void PrintPrediction(float value, SsaSpikePrediction
            prediction) =>
            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value,
            prediction.Prediction[0], prediction.Prediction[1],
            prediction.Prediction[2]);

        class TimeSeriesData
        {
            public float Value;

            public TimeSeriesData(float value)
            {
                Value = value;
            }
        }

        class SsaSpikePrediction
        {
            [VectorType(3)]
            public double[] Prediction { get; set; }
        }
    }
}

Platí pro

DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction)

Upozornění

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

Vytvořte SsaSpikeEstimator, což predikuje špičky v časových řadách pomocí analýzy jednotného 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.SsaSpikeEstimator DetectSpikeBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int pvalueHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference);
public static Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator DetectSpikeBySsa (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int pvalueHistoryLength, int trainingWindowSize, int seasonalityWindowSize, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Microsoft.ML.Transforms.TimeSeries.ErrorFunction errorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference);
[<System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")>]
static member DetectSpikeBySsa : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide * Microsoft.ML.Transforms.TimeSeries.ErrorFunction -> Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator
static member DetectSpikeBySsa : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide * Microsoft.ML.Transforms.TimeSeries.ErrorFunction -> Microsoft.ML.Transforms.TimeSeries.SsaSpikeEstimator
<Extension()>
Public Function DetectSpikeBySsa (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Integer, pvalueHistoryLength As Integer, trainingWindowSize As Integer, seasonalityWindowSize As Integer, Optional side As AnomalySide = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided, Optional errorFunction As ErrorFunction = Microsoft.ML.Transforms.TimeSeries.ErrorFunction.SignedDifference) As SsaSpikeEstimator

Parametry

catalog
TransformsCatalog

Katalog transformace.

outputColumnName
String

Název sloupce, který je výsledkem transformace inputColumnName. Data ve sloupci jsou vektorem Double. Vektor obsahuje 3 prvky: výstraha (nenulová hodnota znamená špičku), nezpracované skóre a p-hodnota.

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 špiček v rozsahu [0, 100].

pvalueHistoryLength
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.

side
AnomalySide

Argument, který určuje, jestli se mají detekovat pozitivní nebo záporné anomálie, nebo obojí.

errorFunction
ErrorFunction

Funkce použitá k výpočtu chyby mezi očekávanou a pozorovanou hodnotou.

Návraty

SsaSpikeEstimator
Atributy
ObsoleteAttribute

Příklady

using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public static class DetectSpikeBySsaBatchPrediction
    {
        // 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 spiking points in the series. 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 a spike
            // within the pattern
            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 spike.
                new TimeSeriesData(100),

                new TimeSeriesData(0),
                new TimeSeriesData(1),
                new TimeSeriesData(2),
                new TimeSeriesData(3),
                new TimeSeriesData(4),
            };

            // Convert data to IDataView.
            var dataView = ml.Data.LoadFromEnumerable(data);

            // Setup estimator arguments
            var inputColumnName = nameof(TimeSeriesData.Value);
            var outputColumnName = nameof(SsaSpikePrediction.Prediction);

            // The transformed data.
            var transformedData = ml.Transforms.DetectSpikeBySsa(outputColumnName,
                inputColumnName, 95.0d, 8, TrainingSize, SeasonalitySize + 1).Fit(
                dataView).Transform(dataView);

            // Getting the data of the newly created column as an IEnumerable of
            // SsaSpikePrediction.
            var predictionColumn = ml.Data.CreateEnumerable<SsaSpikePrediction>(
                transformedData, reuseRowObject: false);

            Console.WriteLine($"{outputColumnName} column obtained " +
                $"post-transformation.");

            Console.WriteLine("Data\tAlert\tScore\tP-Value");
            int k = 0;
            foreach (var prediction in predictionColumn)
                PrintPrediction(data[k++].Value, prediction);

            // Prediction column obtained post-transformation.
            // Data    Alert   Score   P-Value
            // 0       0      -2.53    0.50
            // 1       0      -0.01    0.01
            // 2       0       0.76    0.14
            // 3       0       0.69    0.28
            // 4       0       1.44    0.18
            // 0       0      -1.84    0.17
            // 1       0       0.22    0.44
            // 2       0       0.20    0.45
            // 3       0       0.16    0.47
            // 4       0       1.33    0.18
            // 0       0      -1.79    0.07
            // 1       0       0.16    0.50
            // 2       0       0.09    0.50
            // 3       0       0.08    0.45
            // 4       0       1.31    0.12
            // 100     1      98.21    0.00   <-- alert is on, predicted spike
            // 0       0     -13.83    0.29
            // 1       0      -1.74    0.44
            // 2       0      -0.47    0.46
            // 3       0     -16.50    0.29
            // 4       0     -29.82    0.21
        }

        private static void PrintPrediction(float value, SsaSpikePrediction
            prediction) =>
            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value,
            prediction.Prediction[0], prediction.Prediction[1],
            prediction.Prediction[2]);

        class TimeSeriesData
        {
            public float Value;

            public TimeSeriesData(float value)
            {
                Value = value;
            }
        }

        class SsaSpikePrediction
        {
            [VectorType(3)]
            public double[] Prediction { get; set; }
        }
    }
}

Platí pro