TimeSeriesCatalog.DetectChangePointBySsa 方法

参考

定义

命名空间:: Microsoft.ML

程序集:: Microsoft.ML.TimeSeries.dll

包:: Microsoft.ML.TimeSeries v3.0.1

包:: Microsoft.ML.TimeSeries v1.5.5

包:: Microsoft.ML.TimeSeries v1.6.0

包:: Microsoft.ML.TimeSeries v1.7.0

包:: Microsoft.ML.TimeSeries v2.0.0

包:: Microsoft.ML.TimeSeries v1.2.0

包:: Microsoft.ML.TimeSeries v1.3.1

包:: Microsoft.ML.TimeSeries v1.4.0

重要

一些信息与预发行产品相关，相应产品在发行之前可能会进行重大修改。对于此处提供的信息，Microsoft 不作任何明示或暗示的担保。

重载

DetectChangePointBySsa(TransformsCatalog, String, String, Double, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)	创建 SsaChangePointEstimator，它使用单一光谱分析 (SSA) 预测时序的变化点。
DetectChangePointBySsa(TransformsCatalog, String, String, Int32, Int32, Int32, Int32, ErrorFunction, MartingaleType, Double)	已过时. 创建 SsaChangePointEstimator，它使用单一光谱分析 (SSA) 预测时序的变化点。

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

创建 SsaChangePointEstimator，它使用单一光谱分析 (SSA) 预测时序的变化点。

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

参数

catalog: TransformsCatalog

转换的目录。

outputColumnName: String

由转换 inputColumnName生成的列的名称。列数据是一个向量 Double。矢量包含 4 个元素：警报 (非零值表示更改点) 、原始分数、p 值和马丁加尔分数。

inputColumnName: String

要转换的列的名称。列数据必须是 Single。 If set to null, the value of the outputColumnName will be used as source.

confidence: Double

[0， 100] 范围内更改点检测的置信度。

changeHistoryLength: Int32

用于计算 p 值的滑动窗口的大小。

trainingWindowSize: Int32

用于训练的序列开头的点数。

seasonalityWindowSize: Int32

输入时序中最大相关季节性的上限。

errorFunction: ErrorFunction

用于计算预期值和观察到值之间的错误的函数。

martingale: MartingaleType

用于评分的马丁格尔。

eps: Double

Power martingale 的 epsilon 参数。

SsaChangePointEstimator

示例

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;
            }
        }
    }
}

适用于

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

注意

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