public static IEstimator <ITransformer> _ForecastBySsa(this MLContext MLContext, JToken componentObject)
{
string outputColumn = componentObject.Value <string>("OutputColumnName");
string inputColumn = componentObject.Value <string>("InputColumnName");
int windowSize = componentObject.Value <int>("WindowSize");
int seriesLength = componentObject.Value <int>("SeriesLength");
int trainSize = componentObject.Value <int>("TrainSize");
int horizon = componentObject.Value <int>("Horizon");
bool isAdaptive = componentObject.Value <bool>("IsAdaptive");
float discountFactor = componentObject.Value <float>("DiscountFactor");
RankSelectionMethod rankSelection = Enum.Parse <RankSelectionMethod>(componentObject.Value <string>("RankSelectionMethod"));
int rank = componentObject.Value <int>("Rank");
int maxRank = componentObject.Value <int>("MaximumRank");
bool shouldStabilize = componentObject.Value <bool>("ShouldStabilize");
bool shouldMaintain = componentObject.Value <bool>("ShouldMaintainInfo");
GrowthRatio maxGrowth = new GrowthRatio()
{
Growth = componentObject.Value <double>("Growth"), TimeSpan = componentObject.Value <int>("TimeSpan")
};
string lowerBoundColumn = componentObject.Value <string>("ConfidenceLowerBoundColumn");
string upperBoundColumn = componentObject.Value <string>("ConfidenceUpperBoundColumn");
float confidenceLevel = componentObject.Value <float>("ConfidenceLevel");
bool variableHorizon = componentObject.Value <bool>("VariableHorizon");
return(MLContext.Forecasting.ForecastBySsa(outputColumn, inputColumn, windowSize, seriesLength, trainSize,
horizon, isAdaptive, discountFactor, rankSelection, rank, maxRank,
shouldStabilize, shouldMaintain, maxGrowth, lowerBoundColumn,
upperBoundColumn, confidenceLevel, variableHorizon));
}
/// <summary>
/// Singular Spectrum Analysis (SSA) model for univariate time-series forecasting.
/// For the details of the model, refer to http://arxiv.org/pdf/1206.6910.pdf.
/// </summary>
/// <param name="catalog">Catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
/// The vector contains Alert, Raw Score, P-Value as first three values.</param>
/// <param name="windowSize">The length of the window on the series for building the trajectory matrix (parameter L).</param>
/// <param name="seriesLength">The length of series that is kept in buffer for modeling (parameter N).</param>
/// <param name="trainSize">The length of series from the begining used for training.</param>
/// <param name="horizon">The number of values to forecast.</param>
/// <param name="isAdaptive">The flag determing whether the model is adaptive.</param>
/// <param name="discountFactor">The discount factor in [0,1] used for online updates.</param>
/// <param name="rankSelectionMethod">The rank selection method.</param>
/// <param name="rank">The desired rank of the subspace used for SSA projection (parameter r). This parameter should be in the range in [1, windowSize].
/// If set to null, the rank is automatically determined based on prediction error minimization.</param>
/// <param name="maxRank">The maximum rank considered during the rank selection process. If not provided (i.e. set to null), it is set to windowSize - 1.</param>
/// <param name="shouldStabilize">The flag determining whether the model should be stabilized.</param>
/// <param name="shouldMaintainInfo">The flag determining whether the meta information for the model needs to be maintained.</param>
/// <param name="maxGrowth">The maximum growth on the exponential trend.</param>
/// <param name="confidenceLowerBoundColumn">The name of the confidence interval lower bound column. If not specified then confidence intervals will not be calculated.</param>
/// <param name="confidenceUpperBoundColumn">The name of the confidence interval upper bound column. If not specified then confidence intervals will not be calculated.</param>
/// <param name="confidenceLevel">The confidence level for forecasting.</param>
/// <param name="variableHorizon">Set this to true if horizon will change after training(at prediction time).</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// [!code-csharp[ForecastingWithConfidenceInterval](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs)]
/// ]]>
/// </format>
/// </example>
public static SsaForecastingEstimator ForecastBySsa(
this ForecastingCatalog catalog, string outputColumnName, string inputColumnName, int windowSize, int seriesLength, int trainSize, int horizon,
bool isAdaptive = false, float discountFactor = 1, RankSelectionMethod rankSelectionMethod = RankSelectionMethod.Exact, int?rank = null,
int?maxRank = null, bool shouldStabilize = true, bool shouldMaintainInfo = false, GrowthRatio?maxGrowth = null, string confidenceLowerBoundColumn = null,
string confidenceUpperBoundColumn = null, float confidenceLevel = 0.95f, bool variableHorizon = false) =>
new SsaForecastingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, windowSize, seriesLength, trainSize,
horizon, isAdaptive, discountFactor, rankSelectionMethod, rank, maxRank, shouldStabilize, shouldMaintainInfo, maxGrowth, confidenceLowerBoundColumn,
confidenceUpperBoundColumn, confidenceLevel, variableHorizon);
/// <summary>
/// Create a new instance of <see cref="SsaForecastingEstimator"/>
/// </summary>
/// <param name="env">Host Environment.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
/// The vector contains Alert, Raw Score, P-Value as first three values.</param>
/// <param name="windowSize">The length of the window on the series for building the trajectory matrix (parameter L).</param>
/// <param name="seriesLength">The length of series that is kept in buffer for modeling (parameter N).</param>
/// <param name="trainSize">The length of series from the begining used for training.</param>
/// <param name="horizon">The number of values to forecast.</param>
/// <param name="isAdaptive">The flag determing whether the model is adaptive.</param>
/// <param name="discountFactor">The discount factor in [0,1] used for online updates.</param>
/// <param name="rankSelectionMethod">The rank selection method.</param>
/// <param name="rank">The desired rank of the subspace used for SSA projection (parameter r). This parameter should be in the range in [1, windowSize].
/// If set to null, the rank is automatically determined based on prediction error minimization.</param>
/// <param name="maxRank">The maximum rank considered during the rank selection process. If not provided (i.e. set to null), it is set to windowSize - 1.</param>
/// <param name="shouldStabilize">The flag determining whether the model should be stabilized.</param>
/// <param name="shouldMaintainInfo">The flag determining whether the meta information for the model needs to be maintained.</param>
/// <param name="maxGrowth">The maximum growth on the exponential trend.</param>
/// <param name="confidenceLowerBoundColumn">The name of the confidence interval lower bound column. If not specified then confidence intervals will not be calculated.</param>
/// <param name="confidenceUpperBoundColumn">The name of the confidence interval upper bound column. If not specified then confidence intervals will not be calculated.</param>
/// <param name="confidenceLevel">The confidence level for forecasting.</param>
/// <param name="variableHorizon">Set this to true if horizon will change after training.</param>
internal SsaForecastingEstimator(IHostEnvironment env,
string outputColumnName,
string inputColumnName,
int windowSize,
int seriesLength,
int trainSize,
int horizon,
bool isAdaptive = false,
float discountFactor = 1,
RankSelectionMethod rankSelectionMethod = RankSelectionMethod.Exact,
int?rank = null,
int?maxRank = null,
bool shouldStabilize = true,
bool shouldMaintainInfo = false,
GrowthRatio?maxGrowth = null,
string confidenceLowerBoundColumn = null,
string confidenceUpperBoundColumn = null,
float confidenceLevel = 0.95f,
bool variableHorizon = false)
: this(env, new SsaForecastingTransformer.Options
{
Source = inputColumnName ?? outputColumnName,
Name = outputColumnName,
DiscountFactor = discountFactor,
IsAdaptive = isAdaptive,
WindowSize = windowSize,
RankSelectionMethod = rankSelectionMethod,
Rank = rank,
MaxRank = maxRank,
ShouldStabilize = shouldStabilize,
ShouldMaintainInfo = shouldMaintainInfo,
MaxGrowth = maxGrowth,
ConfidenceLevel = confidenceLevel,
ConfidenceLowerBoundColumn = confidenceLowerBoundColumn,
ConfidenceUpperBoundColumn = confidenceUpperBoundColumn,
SeriesLength = seriesLength,
TrainSize = trainSize,
VariableHorizon = variableHorizon,
Horizon = horizon
})
{
}
/// <summary>
/// Singular Spectrum Analysis (SSA) model for modeling univariate time-series.
/// For the details of the model, refer to http://arxiv.org/pdf/1206.6910.pdf.
/// </summary>
/// <param name="catalog">Catalog.</param>
/// <param name="inputColumnName">The name of the column on which forecasting needs to be performed.</param>
/// <param name="trainSize">The length of series from the begining used for training.</param>
/// <param name="seriesLength">The length of series that is kept in buffer for modeling (parameter N from reference papar).</param>
/// <param name="windowSize">The length of the window on the series for building the trajectory matrix (parameter L from reference papar).</param>
/// <param name="discountFactor">The discount factor in [0,1] used for online updates (default = 1).</param>
/// <param name="rankSelectionMethod">The rank selection method (default = Exact).</param>
/// <param name="rank">The desired rank of the subspace used for SSA projection (parameter r from reference papar). This parameter should be in the range in [1, <paramref name="windowSize"/>].
/// If set to null, the rank is automatically determined based on prediction error minimization. (default = null)</param>
/// <param name="maxRank">The maximum rank considered during the rank selection process. If not provided (i.e. set to null), it is set to <paramref name="windowSize"/> - 1.</param>
/// <param name="shouldComputeForecastIntervals">The flag determining whether the confidence bounds for the point forecasts should be computed. (default = <see langword="true"/>)</param>
/// <param name="shouldstablize">The flag determining whether the model should be stabilized.</param>
/// <param name="shouldMaintainInfo">The flag determining whether the meta information for the model needs to be maintained.</param>
/// <param name="maxGrowth">The maximum growth on the exponential trend</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// [!code-csharp[ForecastingWithConfidenceInterval](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/ForecastingWithConfidenceInterval.cs)]
/// ]]>
/// </format>
/// </example>
public static AdaptiveSingularSpectrumSequenceModeler AdaptiveSingularSpectrumSequenceModeler(this ForecastingCatalog catalog,
string inputColumnName, int trainSize, int seriesLength, int windowSize, Single discountFactor = 1, RankSelectionMethod rankSelectionMethod = RankSelectionMethod.Exact,
int?rank = null, int?maxRank = null, bool shouldComputeForecastIntervals = true, bool shouldstablize = true, bool shouldMaintainInfo = false, GrowthRatio?maxGrowth = null) =>
new AdaptiveSingularSpectrumSequenceModeler(CatalogUtils.GetEnvironment(catalog), inputColumnName, trainSize, seriesLength, windowSize, discountFactor,
rankSelectionMethod, rank, maxRank, shouldComputeForecastIntervals, shouldstablize, shouldMaintainInfo, maxGrowth);
