TimeSeriesCatalog.ForecastBySsa Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.TimeSeries.dll

Pacchetto:

Microsoft.ML.TimeSeries v2.0.0

Modello SSA (Singular Spectrum Analysis) per la previsione di serie temporali univariate. Per i dettagli del modello, fare riferimento a 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

Parametri

catalog

ForecastingCatalog

Catalogo.

outputColumnName

String

Nome della colonna risultante dalla trasformazione di inputColumnName.

inputColumnName

String

Nome della colonna da trasformare. Se impostato su null, il valore di outputColumnName verrà usato come origine. Il vettore contiene Alert, Raw Score, P-Value come primi tre valori.

windowSize

Int32

Lunghezza della finestra della serie per la costruzione della matrice di traiettoria (parametro L).

seriesLength

Int32

Lunghezza della serie mantenuta nel buffer per la modellazione (parametro N).

trainSize

Int32

Lunghezza della serie dall'inizio utilizzata per il training.

horizon

Int32

Numero di valori da prevedere.

isAdaptive

Boolean

Flag che determina se il modello è adattivo.

discountFactor

Single

Fattore di sconto in [0,1] usato per gli aggiornamenti online.

rankSelectionMethod

RankSelectionMethod

Metodo di selezione della classificazione.

rank

Nullable<Int32>

Classificazione desiderata dello spazio secondario usato per la proiezione SSA (parametro r). Questo parametro deve essere compreso nell'intervallo in [1, windowSize]. Se impostato su Null, la classificazione viene determinata automaticamente in base alla riduzione dell'errore di stima.

maxRank

Nullable<Int32>

Classificazione massima considerata durante il processo di selezione della classificazione. Se non viene specificato (ad esempio impostato su Null), viene impostato su windowSize - 1.

shouldStabilize

Boolean

Flag che determina se il modello deve essere stabilizzato.

shouldMaintainInfo

Boolean

Flag che determina se le meta informazioni per il modello devono essere mantenute.

maxGrowth

Nullable<GrowthRatio>

Crescita massima della tendenza esponenziale.

confidenceLowerBoundColumn

String

Nome della colonna con limite inferiore dell'intervallo di confidenza. Se non specificato, gli intervalli di confidenza non verranno calcolati.

confidenceUpperBoundColumn

String

Nome della colonna limite superiore dell'intervallo di confidenza. Se non specificato, gli intervalli di confidenza non verranno calcolati.

confidenceLevel

Single

Livello di confidenza per la previsione.

variableHorizon

Boolean

Impostare questo valore su true se l'orizzonte cambierà dopo il training (in fase di stima).

Restituisce

SsaForecastingEstimator

Esempio

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;
            }
        }
    }
}