Bagikan melalui

TimeSeriesCatalog.DetectSpikeBySsa Metode

  • Referensi

Definisi

Ruang nama:
Microsoft.ML
Rakitan:
Microsoft.ML.TimeSeries.dll
Paket:
Microsoft.ML.TimeSeries v3.0.1
Paket:
Microsoft.ML.TimeSeries v1.5.5
Paket:
Microsoft.ML.TimeSeries v1.6.0
Paket:
Microsoft.ML.TimeSeries v1.7.0
Paket:
Microsoft.ML.TimeSeries v2.0.0
Paket:
Microsoft.ML.TimeSeries v1.2.0
Paket:
Microsoft.ML.TimeSeries v1.3.1
Paket:
Microsoft.ML.TimeSeries v1.4.0

Overload

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

Buat SsaSpikeEstimator, yang memprediksi lonjakan dalam rangkaian waktu menggunakan Analisis Spektrum Tunggal (SSA).
DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction)
Kedaluwarsa.

Buat SsaSpikeEstimator, yang memprediksi lonjakan dalam rangkaian waktu menggunakan Analisis Spektrum Tunggal (SSA).

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

Buat SsaSpikeEstimator, yang memprediksi lonjakan dalam rangkaian waktu menggunakan Analisis Spektrum Tunggal (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

Parameter

catalog
TransformsCatalog

Katalog transformasi.

outputColumnName
String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Data kolom adalah vektor dari Double. Vektor berisi 3 elemen: pemberitahuan (nilai bukan nol berarti lonjakan), skor mentah, dan nilai p.

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 lonjakan dalam rentang [0, 100].

pvalueHistoryLength
Int32

Ukuran jendela geser untuk menghitung nilai p.

trainingWindowSize
Int32

Jumlah poin dari awal urutan yang digunakan untuk pelatihan.

seasonalityWindowSize
Int32

Batas atas pada musiman relevan terbesar dalam rangkaian waktu input.

side
AnomalySide

Argumen yang menentukan apakah akan mendeteksi anomali positif atau negatif, atau keduanya.

errorFunction
ErrorFunction

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

Mengembalikan

SsaSpikeEstimator

Contoh

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; }
        }
    }
}

Berlaku untuk

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

Perhatian

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

Buat SsaSpikeEstimator, yang memprediksi lonjakan 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.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

Parameter

catalog
TransformsCatalog

Katalog transformasi.

outputColumnName
String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Data kolom adalah vektor dari Double. Vektor berisi 3 elemen: pemberitahuan (nilai bukan nol berarti lonjakan), skor mentah, dan nilai p.

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 lonjakan dalam rentang [0, 100].

pvalueHistoryLength
Int32

Ukuran jendela geser untuk menghitung nilai p.

trainingWindowSize
Int32

Jumlah poin dari awal urutan yang digunakan untuk pelatihan.

seasonalityWindowSize
Int32

Batas atas pada musiman relevan terbesar dalam rangkaian waktu input.

side
AnomalySide

Argumen yang menentukan apakah akan mendeteksi anomali positif atau negatif, atau keduanya.

errorFunction
ErrorFunction

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

Mengembalikan

SsaSpikeEstimator
Atribut
ObsoleteAttribute

Contoh

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; }
        }
    }
}

Berlaku untuk