TimeSeriesCatalog.ForecastBySsa Método
Definição
Importante
Algumas informações se referem a produtos de pré-lançamento que podem ser substancialmente modificados antes do lançamento. A Microsoft não oferece garantias, expressas ou implícitas, das informações aqui fornecidas.
Modelo de SSA (Singular Spectrum Analysis) para previsão de série temporal não variável. Para obter os detalhes do 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 SsaForecastingEstimatorParâmetros
- catalog
- ForecastingCatalog
Catálogo.
- outputColumnName
- String
Nome da coluna resultante da transformação de inputColumnName.
- inputColumnName
- String
Nome da coluna a ser transformada. Se definido como null, o valor do outputColumnName será usado como origem.
O vetor contém Alerta, Pontuação Bruta, Valor P como três primeiros valores.
- windowSize
- Int32
O comprimento da janela na série para criar a matriz de trajetória (parâmetro L).
- seriesLength
- Int32
O comprimento da série que é mantido no buffer para modelagem (parâmetro N).
- trainSize
- Int32
O comprimento da série desde o início usado para treinamento.
- horizon
- Int32
O número de valores a serem previstos.
- isAdaptive
- Boolean
O sinalizador que determina se o modelo é adaptável.
- discountFactor
- Single
O fator de desconto em [0,1] usado para atualizações online.
- rankSelectionMethod
- RankSelectionMethod
O método de seleção de classificação.
A classificação desejada do subespaço usado para projeção SSA (parâmetro r). Esse parâmetro deve estar no intervalo em [1, windowSize]. Se definido como nulo, a classificação será determinada automaticamente com base na minimização de erro de previsão.
A classificação máxima considerada durante o processo de seleção de classificação. Se não for fornecido (ou seja, definido como nulo), ele será definido como windowSize - 1.
- shouldStabilize
- Boolean
O sinalizador que determina se o modelo deve ser estabilizado.
- shouldMaintainInfo
- Boolean
O sinalizador que determina se as informações de meta para o modelo precisam ser mantidas.
- maxGrowth
- Nullable<GrowthRatio>
O crescimento máximo na tendência exponencial.
- confidenceLowerBoundColumn
- String
O nome da coluna de limite inferior do intervalo de confiança. Se não for especificado, os intervalos de confiança não serão calculados.
- confidenceUpperBoundColumn
- String
O nome da coluna de limite superior do intervalo de confiança. Se não for especificado, os intervalos de confiança não serão calculados.
- confidenceLevel
- Single
O nível de confiança para previsão.
- variableHorizon
- Boolean
Defina isso como true se o horizonte mudar após o treinamento (no momento da previsão).
Retornos
Exemplos
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;
}
}
}
}
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