TimeSeriesCatalog.DetectSpikeBySsa 메서드

참조

정의

네임스페이스:: Microsoft.ML

어셈블리:: Microsoft.ML.TimeSeries.dll

패키지:: Microsoft.ML.TimeSeries v3.0.1

패키지:: Microsoft.ML.TimeSeries v1.5.5

패키지:: Microsoft.ML.TimeSeries v1.6.0

패키지:: Microsoft.ML.TimeSeries v1.7.0

패키지:: Microsoft.ML.TimeSeries v2.0.0

패키지:: Microsoft.ML.TimeSeries v1.2.0

패키지:: Microsoft.ML.TimeSeries v1.3.1

패키지:: Microsoft.ML.TimeSeries v1.4.0

중요

일부 정보는 릴리스되기 전에 상당 부분 수정될 수 있는 시험판 제품과 관련이 있습니다. Microsoft는 여기에 제공된 정보에 대해 어떠한 명시적이거나 묵시적인 보증도 하지 않습니다.

오버로드

DetectSpikeBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, AnomalySide, ErrorFunction)	SSA(단수 스펙트럼 분석)를 사용하여 시계열의 급증을 예측하는 CreateSsaSpikeEstimator.
DetectSpikeBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, AnomalySide, ErrorFunction)	사용되지 않음. SSA(단수 스펙트럼 분석)를 사용하여 시계열의 급증을 예측하는 CreateSsaSpikeEstimator.

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

SSA(단수 스펙트럼 분석)를 사용하여 시계열의 급증을 예측하는 CreateSsaSpikeEstimator.

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

매개 변수

catalog: TransformsCatalog

변환의 카탈로그입니다.

outputColumnName: String

의 변환에서 생성된 열의 inputColumnName이름입니다. 열 데이터는 벡터입니다 Double. 벡터에는 경고(0이 아닌 값은 스파이크를 의미함), 원시 점수 및 p-값의 3가지 요소가 포함됩니다.

inputColumnName: String

변환할 열의 이름입니다. 열 데이터는 이어야 Single합니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다.

confidence: Double

범위 [0, 100]의 스파이크 검색에 대한 신뢰도입니다.

pvalueHistoryLength: Int32

p-값을 계산하기 위한 슬라이딩 윈도우의 크기입니다.

trainingWindowSize: Int32

학습에 사용되는 시퀀스의 시작 지점 수입니다.

seasonalityWindowSize: Int32

입력 시계열에서 가장 큰 관련 계절성에 대한 상한입니다.

side: AnomalySide

양수 또는 음수 변칙을 검색할지 또는 둘 다를 검색할지를 결정하는 인수입니다.

errorFunction: ErrorFunction

예상 값과 관찰된 값 사이의 오류를 계산하는 데 사용되는 함수입니다.

반환

SsaSpikeEstimator

예제

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; }
        }
    }
}

적용 대상

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

주의

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

SSA(단수 스펙트럼 분석)를 사용하여 시계열의 급증을 예측하는 CreateSsaSpikeEstimator.

[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

매개 변수

catalog: TransformsCatalog

변환의 카탈로그입니다.

outputColumnName: String

inputColumnName: String

변환할 열의 이름입니다. 열 데이터는 이어야 Single합니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다.

confidence: Int32

범위 [0, 100]의 스파이크 검색에 대한 신뢰도입니다.

pvalueHistoryLength: Int32

p-값을 계산하기 위한 슬라이딩 윈도우의 크기입니다.

trainingWindowSize: Int32

학습에 사용되는 시퀀스의 시작 지점 수입니다.

seasonalityWindowSize: Int32

입력 시계열에서 가장 큰 관련 계절성에 대한 상한입니다.

side: AnomalySide

양수 또는 음수 변칙을 검색할지 또는 둘 다를 검색할지를 결정하는 인수입니다.

errorFunction: ErrorFunction

예상 값과 관찰된 값 사이의 오류를 계산하는 데 사용되는 함수입니다.

반환

SsaSpikeEstimator

특성: ObsoleteAttribute

예제

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; }
        }
    }
}

적용 대상

다음을 통해 공유

TimeSeriesCatalog.DetectSpikeBySsa 메서드

정의

오버로드

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

매개 변수

반환

예제

적용 대상

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

매개 변수

반환

예제

적용 대상

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