TimeSeriesCatalog.ForecastBySsa Metoda

Definice

Obor názvů:

Microsoft.ML

Sestavení:

Microsoft.ML.TimeSeries.dll

Balíček:

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

Zdroj:

ExtensionsCatalog.cs

Model SSA (Singular Spectrum Analysis) pro jednovariátní prognózování časových řad Podrobnosti o modelu najdete v http://arxiv.org/pdf/1206.6910.pdftématu .

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

Parametry

catalog

ForecastingCatalog

Katalog.

outputColumnName

String

Název sloupce, který je výsledkem transformace inputColumnName.

inputColumnName

String

Název sloupce, který se má transformovat. Pokud je nastavená null hodnota outputColumnName, použije se jako zdroj. Vektor obsahuje výstrahy, nezpracované skóre, P-Value jako první tři hodnoty.

windowSize

Int32

Délka okna na řadě pro vytvoření trajektorie matice (parametr L).

seriesLength

Int32

Délka řady, která je uložena ve vyrovnávací paměti pro modelování (parametr N).

trainSize

Int32

Délka řady od začátku použitého pro trénování.

horizon

Int32

Počet hodnot, které se mají předpovídat.

isAdaptive

Boolean

Příznak určující, jestli je model adaptivní.

discountFactor

Single

Faktor slevy [0,1] použitý pro online aktualizace.

rankSelectionMethod

RankSelectionMethod

Metoda výběru pořadí.

rank

Nullable<Int32>

Požadované pořadí podprostoru použitého pro projekce SSA (parametr r). Tento parametr by měl být v rozsahu [1, windowSize]. Pokud je nastavená hodnota null, pořadí se automaticky určí na základě minimalizace predikce chyby.

maxRank

Nullable<Int32>

Maximální pořadí považované během procesu výběru pořadí. Pokud není zadaný (tj. nastaven na hodnotu null), nastaví se na hodnotu windowSize - 1.

shouldStabilize

Boolean

Příznak určující, zda má být model stabilizován.

shouldMaintainInfo

Boolean

Příznak určující, jestli je potřeba zachovat metadata modelu.

maxGrowth

Nullable<GrowthRatio>

Maximální růst exponenciálního trendu.

confidenceLowerBoundColumn

String

Název sloupce s dolní vazbou intervalu spolehlivosti. Pokud nezadáte, intervaly spolehlivosti nebudou vypočítány.

confidenceUpperBoundColumn

String

Název sloupce s horní mezí intervalu spolehlivosti. Pokud nezadáte, intervaly spolehlivosti nebudou vypočítány.

confidenceLevel

Single

Úroveň spolehlivosti pro prognózování

variableHorizon

Boolean

Tuto hodnotu nastavte na true, pokud se horizont po trénování (v době předpovědi) změní.

Návraty

SsaForecastingEstimator

Příklady

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;
            }
        }
    }
}