TimeSeriesCatalog.ForecastBySsa Methode

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.TimeSeries.dll

Paket:

Microsoft.ML.TimeSeries v5.0.0-preview.1.25125.4

Quelle:

ExtensionsCatalog.cs

Singular Spectrum Analysis (SSA)-Modell für univariate Zeitreihenprognosen. 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

Name der Spalte, die sich aus der Transformation von inputColumnName.

inputColumnName

String

Name der zu transformierenden Spalte. Bei Festlegung auf null, wird der Wert des Werts outputColumnName als Quelle verwendet. Der Vektor enthält Alert, Raw Score, P-Value als erste drei Werte.

windowSize

Int32

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

seriesLength

Int32

Die Länge der Datenreihe, die im Puffer für die Modellierung (Parameter N) gespeichert wird.

trainSize

Int32

Die Länge der Serie von Anfang an, die für die Schulung verwendet wird.

horizon

Int32

Die Anzahl der zu prognostizierenden Werte.

isAdaptive

Boolean

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

discountFactor

Single

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

rankSelectionMethod

RankSelectionMethod

Die Bewertungsauswahlmethode.

rank

Nullable<Int32>

Die gewünschte Rangfolge des Unterbereichs, der für die SSA-Projektion (Parameter r) verwendet wird. Dieser Parameter sollte sich im Bereich in [1, windowSize] befinden. Bei Festlegung auf NULL wird der Rang automatisch basierend auf der Minimierung des Vorhersagefehlers bestimmt.

maxRank

Nullable<Int32>

Die maximale Rangfolge, die während des Rangauswahlprozesses berücksichtigt wird. Wenn nicht angegeben (d. h. auf NULL festgelegt), wird sie auf windowSize - 1 festgelegt.

shouldStabilize

Boolean

Das Kennzeichen, 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 Konfidenzintervallspalte, die untere Grenze ist. Wenn nicht angegeben, werden Konfidenzintervalle nicht berechnet.

confidenceUpperBoundColumn

String

Der Name des Konfidenzintervalls für die obere gebundene Spalte. Wenn nicht angegeben, werden Konfidenzintervalle nicht berechnet.

confidenceLevel

Single

Das Konfidenzniveau für die Prognose.

variableHorizon

Boolean

Legen Sie diesen Wert 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;
            }
        }
    }
}