TimeSeriesModel<T> Class

Represents a time-series model.

Definition

Namespace: Numerics.NET.Statistics.TimeSeriesAnalysis
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 9.0.4

public abstract class TimeSeriesModel<T> : Model
where T : Object, IComparable<T>

Visual Basic

Public MustInherit Class TimeSeriesModel(Of T As {Object, IComparable(Of T)})
	Inherits Model

C++/CLI

generic<typename T>
where T : Object, IComparable<T>
public ref class TimeSeriesModel abstract : public Model

[<AbstractClassAttribute>]
type TimeSeriesModel<'T when 'T : Object and IComparable<'T>> = 
    class
        inherit Model
    end

Inheritance: Object → Model → TimeSeriesModel<T>

Derived: Numerics.NET.Statistics.TimeSeriesAnalysis.ArimaModel
Numerics.NET.Statistics.TimeSeriesAnalysis.ExponentialSmoothingModel
Numerics.NET.Statistics.TimeSeriesAnalysis.GarchModel

Type Parameters

T

Remarks

Use the TimeSeriesModel<T> class as the base class for implementing time series models. This is an abstract base class and cannot be instantiated directly. Instead, use one of the inherited types, as listed in the table below:

Class	Description
ExponentialSmoothingModel	Single or double exponential smoothing.
ArimaModel	Auto-regressive integrated moving average models.

Constructors

TimeSeriesModel<T>

Called by derived classes to constructs a new TimeSeriesModel<T>.

Properties

BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model)
Computed	Gets whether the model has been computed. (Inherited from Model) Obsolete.
CovarianceMatrix	Gets the covariance matrix of the model parameters.
Data	Gets an object that contains all the data used as input to the model. (Inherited from Model)
DegreesOfFreedom	Gets the total degrees of freedom of the data.
Fitted	Gets whether the model has been computed. (Inherited from Model)
InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model)
LogLikelihood	Gets the log-likelihood that the model generated the data.
MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model)
ModelSchema	Gets the collection of variables used in the model. (Inherited from Model)
NumberOfObservations	Gets the number of observations the model is based on. (Inherited from Model)
ParallelOptions	Gets or sets an object that specifies how the calculation of the model should be parallelized. (Inherited from Model)
Parameters	Gets the collection of parameters associated with this model.
ParameterValues	Gets the collection of parameters associated with this model.
Predictions	Gets a vector containing the model's predicted values for the dependent variable.
Residuals	Gets a vector containing the residuals of the model.
ResidualSumOfSquares	Gets the sum of squares of the residuals of the model.
StandardError	Gets the standard error of the regression.
Status	Gets the status of the model, which determines which information is available. (Inherited from Model)
SupportsWeights	Indicates whether the model supports case weights. (Inherited from Model)
TimeSeries	Gets the time series that is being modeled.
Weights	Gets or sets the actual weights. (Inherited from Model)

Methods

Compute()	Computes the model. (Inherited from Model) Obsolete.
Compute(ParallelOptions)	Computes the model. (Inherited from Model) Obsolete.
Equals	Determines whether the specified object is equal to the current object. (Inherited from Object)
Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object)
Fit()	Fits the model to the data. (Inherited from Model)
Fit(ParallelOptions)	Fits the model to the data. (Inherited from Model)
FitCore	Computes the model to the specified input using the specified parallelization options. (Inherited from Model)
Forecast()	Returns the one step ahead forecast.
Forecast(Int32)	Returns the forecast for the specified number of steps ahead.
GetAkaikeInformationCriterion	Returns the Akaike information criterion (AIC) value for the model.
GetBayesianInformationCriterion	Returns the Bayesian information criterion (BIC) value for the model.
GetHashCode	Serves as the default hash function. (Inherited from Object)
GetType	Gets the Type of the current instance. (Inherited from Object)
MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object)
ResetComputation	Clears all fitted model parameters. (Inherited from Model) Obsolete.
ResetFit	Clears all fitted model parameters. (Inherited from Model)
SetDataSource	Uses the specified data frame as the source for all input variables. (Inherited from Model)
Summarize()	Returns a string containing a human-readable summary of the object using default options. (Inherited from Model)
Summarize(SummaryOptions)	Returns a string containing a human-readable summary of the object using the specified options. (Inherited from Model)
ToString	Returns a string that represents the current object. (Inherited from Model)