/// <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 the ML context.
/// </summary>
/// <param name="seed">Seed for MLContext's random number generator. See the remarks for more details.</param>
/// <remarks>
/// Many operations in ML.NET require randomness, such as
/// random data shuffling, random sampling, random parameter initialization,
/// random permutation, random feature selection, and many more.
/// MLContext's random number generator is the global source of randomness for
/// all of such random operations.
///
/// If a fixed seed is provided by <paramref name="seed"/>, MLContext environment becomes
/// deterministic, meaning that the results are repeatable and will remain the same across multiple runs.
/// For instance in many of ML.NET's API reference example code snippets, a seed is provided.
/// That's because we want the users to get the same output as what's included in example comments,
/// when they run the example on their own machine.
///
/// Generally though, repeatability is not a requirement and that's the default behavior.
/// If a seed is not provided by <paramref name="seed"/>, i.e. it's set to <see langword="null"/>,
/// MLContext environment becomes non-deterministic and outputs change across multiple runs.
///
/// There are many operations in ML.NET that don't use any randomness, such as
/// min-max normalization, concatenating columns, missing value indication, etc.
/// The behavior of those operations are deterministic regardless of the seed value.
///
/// Also ML.NET trainers don't use randomness *after* the training is finished.
/// So, the predictions from a loaded model don't depend on the seed value.
/// </remarks>
public MLContext(int?seed = null)
{
_env = new LocalEnvironment(seed);
_env.AddListener(ProcessMessage);
BinaryClassification = new BinaryClassificationCatalog(_env);
MulticlassClassification = new MulticlassClassificationCatalog(_env);
Regression = new RegressionCatalog(_env);
Clustering = new ClusteringCatalog(_env);
Ranking = new RankingCatalog(_env);
AnomalyDetection = new AnomalyDetectionCatalog(_env);
Forecasting = new ForecastingCatalog(_env);
Transforms = new TransformsCatalog(_env);
Model = new ModelOperationsCatalog(_env);
Data = new DataOperationsCatalog(_env);
}
internal Forecasters(ForecastingCatalog catalog)
: base(catalog)
{
}
/// <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);