TimeSeriesCatalog.ForecastBySsa Método
Definición
Importante
Parte de la información hace referencia a la versión preliminar del producto, que puede haberse modificado sustancialmente antes de lanzar la versión definitiva. Microsoft no otorga ninguna garantía, explícita o implícita, con respecto a la información proporcionada aquí.
Modelo de análisis de espectro singular (SSA) para la previsión de series temporales univariante. Para obtener los detalles del modelo, consulte 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 SsaForecastingEstimatorParámetros
- catalog
- ForecastingCatalog
Catálogo.
- outputColumnName
- String
Nombre de la columna resultante de la transformación de inputColumnName.
- inputColumnName
- String
Nombre de columna que se va a transformar. Si se establece en null, el valor de outputColumnName se usará como origen.
El vector contiene Alert, Raw Score, P-Value como los tres primeros valores.
- windowSize
- Int32
Longitud de la ventana de la serie para crear la matriz de trayectoria (parámetro L).
- seriesLength
- Int32
Longitud de la serie que se mantiene en el búfer para el modelado (parámetro N).
- trainSize
- Int32
Longitud de la serie desde el principio utilizada para el entrenamiento.
- horizon
- Int32
Número de valores que se van a predecir.
- isAdaptive
- Boolean
Marca que determina si el modelo es adaptable.
- discountFactor
- Single
Factor de descuento en [0,1] usado para las actualizaciones en línea.
- rankSelectionMethod
- RankSelectionMethod
Método de selección de clasificación.
Clasificación deseada del subespacio usado para la proyección SSA (parámetro r). Este parámetro debe estar en el intervalo de [1, windowSize]. Si se establece en null, la clasificación se determina automáticamente en función de la minimización de errores de predicción.
Clasificación máxima que se considera durante el proceso de selección de clasificación. Si no se proporciona (es decir, se establece en null), se establece en windowSize - 1.
- shouldStabilize
- Boolean
Marca que determina si se debe estabilizar el modelo.
- shouldMaintainInfo
- Boolean
Marca que determina si es necesario mantener la meta información del modelo.
- maxGrowth
- Nullable<GrowthRatio>
El crecimiento máximo en la tendencia exponencial.
- confidenceLowerBoundColumn
- String
Nombre de la columna de límite inferior del intervalo de confianza. Si no se especifica, no se calcularán los intervalos de confianza.
- confidenceUpperBoundColumn
- String
Nombre de la columna de límite superior del intervalo de confianza. Si no se especifica, no se calcularán los intervalos de confianza.
- confidenceLevel
- Single
Nivel de confianza para la previsión.
- variableHorizon
- Boolean
Establézcalo en true si horizon cambiará después del entrenamiento (en el momento de la predicción).
Devoluciones
Ejemplos
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;
}
}
}
}