TimeSeriesCatalog.ForecastBySsa Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.TimeSeries.dll

Paket:

Microsoft.ML.TimeSeries v3.0.1

SSA-Modell (Singular Spectrum Analysis) für univariate Zeitreihenvorhersagen. Details zum Modell finden Sie unter http://arxiv.org/pdf/1206.6910.pdf.

public static Microsoft.ML.Transforms.TimeSeries.SsaForecastingEstimator ForecastBySsa (this Microsoft.ML.ForecastingCatalog catalog, string outputColumnName, string inputColumnName, int windowSize, int seriesLength, int trainSize, int horizon, bool isAdaptive = false, float discountFactor = 1, Microsoft.ML.Transforms.TimeSeries.RankSelectionMethod rankSelectionMethod = Microsoft.ML.Transforms.TimeSeries.RankSelectionMethod.Exact, int? rank = default, int? maxRank = default, bool shouldStabilize = true, bool shouldMaintainInfo = false, Microsoft.ML.Transforms.TimeSeries.GrowthRatio? maxGrowth = default, string confidenceLowerBoundColumn = default, string confidenceUpperBoundColumn = default, float confidenceLevel = 0.95, bool variableHorizon = false);

static member ForecastBySsa : Microsoft.ML.ForecastingCatalog * string * string * int * int * int * int * bool * single * Microsoft.ML.Transforms.TimeSeries.RankSelectionMethod * Nullable<int> * Nullable<int> * bool * bool * Nullable<Microsoft.ML.Transforms.TimeSeries.GrowthRatio> * string * string * single * bool -> Microsoft.ML.Transforms.TimeSeries.SsaForecastingEstimator

<Extension()>
Public Function ForecastBySsa (catalog As ForecastingCatalog, outputColumnName As String, inputColumnName As String, windowSize As Integer, seriesLength As Integer, trainSize As Integer, horizon As Integer, Optional isAdaptive As Boolean = false, Optional discountFactor As Single = 1, Optional rankSelectionMethod As RankSelectionMethod = Microsoft.ML.Transforms.TimeSeries.RankSelectionMethod.Exact, Optional rank As Nullable(Of Integer) = Nothing, Optional maxRank As Nullable(Of Integer) = Nothing, Optional shouldStabilize As Boolean = true, Optional shouldMaintainInfo As Boolean = false, Optional maxGrowth As Nullable(Of GrowthRatio) = Nothing, Optional confidenceLowerBoundColumn As String = Nothing, Optional confidenceUpperBoundColumn As String = Nothing, Optional confidenceLevel As Single = 0.95, Optional variableHorizon As Boolean = false) As SsaForecastingEstimator

Parameter

catalog

ForecastingCatalog

Katalog.

outputColumnName

String

Der Name der Spalte, die sich aus der Transformation von inputColumnNameergibt.

inputColumnName

String

Name der zu transformierenden Spalte. Wenn auf nullfestgelegt ist, wird der Wert von outputColumnName als Quelle verwendet. Der Vektor enthält Warnung, Rohwert und P-Wert als erste drei Werte.

windowSize

Int32

Die Länge des Fensters der Reihe zum Erstellen der Trajektorienmatrix (Parameter L).

seriesLength

Int32

Die Länge von Reihen, die für die Modellierung im Puffer aufbewahrt werden (Parameter N).

trainSize

Int32

Die Länge von Reihen vom Anfang an, die für das Training verwendet werden.

horizon

Int32

Die Anzahl der zu prognostizierenden Werte.

isAdaptive

Boolean

Das Flag, das bestimmt, ob das Modell adaptiv ist.

discountFactor

Single

Der Rabattfaktor in [0,1], der für Onlineupdates verwendet wird.

rankSelectionMethod

RankSelectionMethod

Die Rangauswahlmethode.

rank

Nullable<Int32>

Der gewünschte Rang des für die SSA-Projektion verwendeten Unterraums (Parameter r). Dieser Parameter sollte im Bereich in [1, windowSize] liegen. Wenn sie auf NULL festgelegt ist, wird der Rang automatisch basierend auf der Minimierung von Vorhersagefehlern bestimmt.

maxRank

Nullable<Int32>

Der maximale Rang, der während des Rangauswahlprozesses berücksichtigt wird. Wenn nicht angegeben (d. h. auf NULL festgelegt), wird es auf windowSize - 1 festgelegt.

shouldStabilize

Boolean

Das Flag, das bestimmt, ob das Modell stabilisiert werden soll.

shouldMaintainInfo

Boolean

Das Flag, das bestimmt, ob die Metainformationen für das Modell beibehalten werden müssen.

maxGrowth

Nullable<GrowthRatio>

Das maximale Wachstum im exponentiellen Trend.

confidenceLowerBoundColumn

String

Der Name der unteren Begrenzungsspalte des Konfidenzintervalls. Wenn nicht angegeben, werden konfidenzintervalle nicht berechnet.

confidenceUpperBoundColumn

String

Der Name der oberen Begrenzungsspalte des Konfidenzintervalls. Wenn nicht angegeben, werden konfidenzintervalle nicht berechnet.

confidenceLevel

Single

Das Konfidenzniveau für Vorhersagen.

variableHorizon

Boolean

Legen Sie dies auf true fest, wenn sich der Horizont nach dem Training (zur Vorhersagezeit) ändert.

Gibt zurück

SsaForecastingEstimator

Beispiele

using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
    public static class Forecasting
    {
        // This example creates a time series (list of Data with the i-th element
        // corresponding to the i-th time slot) and then does forecasting.
        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.
            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),
            };

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

            // Setup arguments.
            var inputColumnName = nameof(TimeSeriesData.Value);
            var outputColumnName = nameof(ForecastResult.Forecast);

            // Instantiate the forecasting model.
            var model = ml.Forecasting.ForecastBySsa(outputColumnName,
                inputColumnName, 5, 11, data.Count, 5);

            // Train.
            var transformer = model.Fit(dataView);

            // Forecast next five values.
            var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData,
                ForecastResult>(ml);

            var forecast = forecastEngine.Predict();

            Console.WriteLine($"Forecasted values:");
            Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
            // Forecasted values:
            // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]

            // Update with new observations.
            forecastEngine.Predict(new TimeSeriesData(0));
            forecastEngine.Predict(new TimeSeriesData(0));
            forecastEngine.Predict(new TimeSeriesData(0));
            forecastEngine.Predict(new TimeSeriesData(0));

            // Checkpoint.
            forecastEngine.CheckPoint(ml, "model.zip");

            // Load the checkpointed model from disk.
            // Load the model.
            ITransformer modelCopy;
            using (var file = File.OpenRead("model.zip"))
                modelCopy = ml.Model.Load(file, out DataViewSchema schema);

            // We must create a new prediction engine from the persisted model.
            var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<
                TimeSeriesData, ForecastResult>(ml);

            // Forecast with the checkpointed model loaded from disk.
            forecast = forecastEngineCopy.Predict();
            Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
            // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]

            // Forecast with the original model(that was checkpointed to disk).
            forecast = forecastEngine.Predict();
            Console.WriteLine("[{0}]", string.Join(", ", forecast.Forecast));
            // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]

        }

        class ForecastResult
        {
            public float[] Forecast { get; set; }
        }

        class TimeSeriesData
        {
            public float Value;

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

using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Transforms.TimeSeries;

namespace Samples.Dynamic
{
    public static class ForecastingWithConfidenceInternal
    {
        // This example creates a time series (list of Data with the i-th element
        // corresponding to the i-th time slot) and then does forecasting.
        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.
            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),
            };

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

            // Setup arguments.
            var inputColumnName = nameof(TimeSeriesData.Value);
            var outputColumnName = nameof(ForecastResult.Forecast);

            // Instantiate the forecasting model.
            var model = ml.Forecasting.ForecastBySsa(outputColumnName,
                inputColumnName, 5, 11, data.Count, 5,
                confidenceLevel: 0.95f,
                confidenceLowerBoundColumn: "ConfidenceLowerBound",
                confidenceUpperBoundColumn: "ConfidenceUpperBound");

            // Train.
            var transformer = model.Fit(dataView);

            // Forecast next five values.
            var forecastEngine = transformer.CreateTimeSeriesEngine<TimeSeriesData,
                ForecastResult>(ml);

            var forecast = forecastEngine.Predict();

            PrintForecastValuesAndIntervals(forecast.Forecast, forecast
                .ConfidenceLowerBound, forecast.ConfidenceUpperBound);
            // Forecasted values:
            // [1.977226, 1.020494, 1.760543, 3.437509, 4.266461]
            // Confidence intervals:
            // [0.3451088 - 3.609343] [-0.7967533 - 2.83774] [-0.058467 - 3.579552] [1.61505 - 5.259968] [2.349299 - 6.183623]

            // Update with new observations.
            forecastEngine.Predict(new TimeSeriesData(0));
            forecastEngine.Predict(new TimeSeriesData(0));
            forecastEngine.Predict(new TimeSeriesData(0));
            forecastEngine.Predict(new TimeSeriesData(0));

            // Checkpoint.
            forecastEngine.CheckPoint(ml, "model.zip");

            // Load the checkpointed model from disk.
            // Load the model.
            ITransformer modelCopy;
            using (var file = File.OpenRead("model.zip"))
                modelCopy = ml.Model.Load(file, out DataViewSchema schema);

            // We must create a new prediction engine from the persisted model.
            var forecastEngineCopy = modelCopy.CreateTimeSeriesEngine<
                TimeSeriesData, ForecastResult>(ml);

            // Forecast with the checkpointed model loaded from disk.
            forecast = forecastEngineCopy.Predict();
            PrintForecastValuesAndIntervals(forecast.Forecast, forecast
                .ConfidenceLowerBound, forecast.ConfidenceUpperBound);

            // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
            // Confidence intervals:
            // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]

            // Forecast with the original model(that was checkpointed to disk).
            forecast = forecastEngine.Predict();
            PrintForecastValuesAndIntervals(forecast.Forecast,
                forecast.ConfidenceLowerBound, forecast.ConfidenceUpperBound);

            // [1.791331, 1.255525, 0.3060154, -0.200446, 0.5657795]
            // Confidence intervals:
            // [0.1592142 - 3.423448] [-0.5617217 - 3.072772] [-1.512994 - 2.125025] [-2.022905 - 1.622013] [-1.351382 - 2.482941]
        }

        static void PrintForecastValuesAndIntervals(float[] forecast, float[]
            confidenceIntervalLowerBounds, float[] confidenceIntervalUpperBounds)
        {
            Console.WriteLine($"Forecasted values:");
            Console.WriteLine("[{0}]", string.Join(", ", forecast));
            Console.WriteLine($"Confidence intervals:");
            for (int index = 0; index < forecast.Length; index++)
                Console.Write($"[{confidenceIntervalLowerBounds[index]} -" +
                    $" {confidenceIntervalUpperBounds[index]}] ");
            Console.WriteLine();
        }

        class ForecastResult
        {
            public float[] Forecast { get; set; }
            public float[] ConfidenceLowerBound { get; set; }
            public float[] ConfidenceUpperBound { get; set; }
        }

        class TimeSeriesData
        {
            public float Value;

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