TimeSeriesCatalog.ForecastBySsa Méthode
Définition
Important
Certaines informations portent sur la préversion du produit qui est susceptible d’être en grande partie modifiée avant sa publication. Microsoft exclut toute garantie, expresse ou implicite, concernant les informations fournies ici.
Modèle SSA (Singular Spectrum Analysis) pour la prévision de séries chronologiques univariées. Pour plus d’informations sur le modèle, reportez-vous à 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 SsaForecastingEstimatorParamètres
- catalog
- ForecastingCatalog
Catalogue.
- outputColumnName
- String
Nom de la colonne résultant de la transformation de inputColumnName.
- inputColumnName
- String
Nom de la colonne à transformer. Si la valeur est définie nullsur , la valeur de outputColumnName sera utilisée comme source.
Le vecteur contient Alert, Raw Score et P-Value comme trois premières valeurs.
- windowSize
- Int32
Longueur de la fenêtre sur la série pour la création de la matrice de trajectoire (paramètre L).
- seriesLength
- Int32
Longueur de série conservée dans la mémoire tampon pour la modélisation (paramètre N).
- trainSize
- Int32
Longueur de série depuis le début utilisée pour l’entraînement.
- horizon
- Int32
Nombre de valeurs à prévoir.
- isAdaptive
- Boolean
Indicateur déterminant si le modèle est adaptatif.
- discountFactor
- Single
Facteur de remise dans [0,1] utilisé pour les mises à jour en ligne.
- rankSelectionMethod
- RankSelectionMethod
Méthode de sélection de classement.
Rang souhaité du sous-espace utilisé pour la projection SSA (paramètre r). Ce paramètre doit se trouver dans la plage dans [1, windowSize]. Si la valeur est null, le classement est automatiquement déterminé en fonction de la réduction des erreurs de prédiction.
Classement maximal pris en compte pendant le processus de sélection de classement. S’il n’est pas fourni (c’est-à-dire défini sur null), il est défini sur windowSize - 1.
- shouldStabilize
- Boolean
Indicateur déterminant si le modèle doit être stabilisé.
- shouldMaintainInfo
- Boolean
Indicateur déterminant si les méta-informations du modèle doivent être conservées.
- maxGrowth
- Nullable<GrowthRatio>
Croissance maximale sur la tendance exponentielle.
- confidenceLowerBoundColumn
- String
Nom de la colonne de limite inférieure de l’intervalle de confiance. S’ils ne sont pas spécifiés, les intervalles de confiance ne sont pas calculés.
- confidenceUpperBoundColumn
- String
Nom de la colonne de limite supérieure de l’intervalle de confiance. S’ils ne sont pas spécifiés, les intervalles de confiance ne sont pas calculés.
- confidenceLevel
- Single
Niveau de confiance pour la prévision.
- variableHorizon
- Boolean
Définissez cette valeur sur true si l’horizon change après l’entraînement (au moment de la prédiction).
Retours
Exemples
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;
}
}
}
}
S’applique à
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