This is really like an ARMA model in many respects, but you specify the square root of the variance (sigma), and the autocorrelations at lags 1...maxLag, instead of specifying autoregressive and moving average polynomials. This means that we have to fall back on the Durbin-Levinson recursions for likelihood computations, instead of using more efficient recursions from Brockwell & Davis. More...

Inheritance diagram for CronoSeries.TimeSeries.Models.ACFModel:

Public Member Functions
	ACFModel (int maxLag)

void	CarryOutPreMLEComputations ()

virtual MathNet.Numerics.LinearAlgebra.Vector< double >	ParameterToCube (MathNet.Numerics.LinearAlgebra.Vector< double > param)

virtual MathNet.Numerics.LinearAlgebra.Vector< double >	CubeToParameter (MathNet.Numerics.LinearAlgebra.Vector< double > cube)

virtual void	ResetRealTimePrediction ()

virtual void	Register (Data.TimeSeries series)

virtual double	Register (DateTime timeStamp, double value, double[] auxValues)

virtual double	Register (DateTime timeStamp, double value)

virtual DistributionSummary	GetCurrentPredictor (DateTime futureTime)

double	Rho (int lag)

void	SetRho (int lag, double value)

override int	NumOutputs ()

override object	GetOutput (int socket)

override string	GetOutputName (int index)

override string	GetParameterName (int index)

override string	GetParameterDescription (int index)

override string	GetShortDescription ()

override double	LogLikelihood (MathNet.Numerics.LinearAlgebra.Vector< double > parameter, double penaltyFactor, bool fillOutputs)

override Data.TimeSeries	SimulateData (List< DateTime > inputs, int randomSeed)
	This function must simulate from the current model. More...

override object	BuildForecasts (object otherData, object inputs)
	This function generates forecasts (or fitted values) for the specified inputs, based on the existing data object and current model parameters. Results are returned in a form that depends on the model, for example, as a TimeSeries of DistributionSummary objects More...

override MathNet.Numerics.LinearAlgebra.Vector< double >	ComputeACF (int outToLag, bool normalize)

int	NumAuxiliaryFunctions ()

string	AuxiliaryFunctionName (int index)

string	AuxiliaryFunctionHelp (int index)

bool	AuxiliaryFunction (int index, out object output)

Public Member Functions inherited from CronoSeries.TimeSeries.Models.UnivariateTimeSeriesModel
override List< Type >	GetAllowedInputTypesFor (int socket)

override List< Type >	GetOutputTypesFor (int socket)

Public Member Functions inherited from CronoSeries.TimeSeries.Models.TimeSeriesModel
override int	NumInputs ()

override string	GetInputName (int socket)

override bool	InputIsFree (int socket)

override bool	SetInput (int socket, object item, StringBuilder failMsg)

override int	NumOutputs ()

override object	GetOutput (int socket)

override string	GetOutputName (int socket)

Public Member Functions inherited from CronoSeries.TimeSeries.Models.Model
string	GetDescription ()

bool	CanUseMLE ()

bool	CanUseMoM ()

bool	CanHandleNaNs ()

abstract double	LogLikelihood (Vector< double > parameter, double penaltyFactor, bool fillOutputs)
	Returns the (possibly penalized) log-likelihood of the model with specified parameters and the current object theData. If parameter==null, it will use CURRENT parameters. If fillOutputs is true, then the residuals and any other outputs will be filled in. More...

virtual void	FitByMLE (int numIterationsLDS, int numIterationsOpt, double consistencyPenalty, Optimizer.OptimizationCallback optCallback)

virtual void	FitByMoM ()
	Fits model by method of moments. This is the default method used of CanUseMLE is false. More...

bool	InputToOutputIsValid ()

bool	SetParameters (Vector< double > v)

Public Member Functions inherited from CronoSeries.TimeSeries.Models.IMLEEstimable
Vector< double >	ParameterToCube (Vector< double > param)

Vector< double >	CubeToParameter (Vector< double > cube)

Public Member Functions inherited from CronoSeries.TimeSeries.Models.IRealTimePredictable
void	Register (Data.TimeSeries series)

Protected Member Functions
override bool	CheckParameterValidity (MathNet.Numerics.LinearAlgebra.Vector< double > param)

MathNet.Numerics.LinearAlgebra.Vector< double >	GetLikelihoodsFromResiduals (double[] res, double[] pvars)

override MathNet.Numerics.LinearAlgebra.Vector< double >	ComputeConsequentialParameters (MathNet.Numerics.LinearAlgebra.Vector< double > parameter)

void	LocalInitializeParameters ()

override void	InitializeParameters ()
	This function is called after OnDataConnection. It can assume valid data is available, and it must fill in valid default parameter values. It typically also sets default parameter states, for purposes of estimation. More...

double[]	ComputeSpecialResiduals (Data.TimeSeries startData, out double[] rs, int forecastHorizon, out double[] forecasts)
	This function uses the Durbin-Levinson algorithm to get one-step predictors and residuals. More...

Protected Member Functions inherited from CronoSeries.TimeSeries.Models.UnivariateTimeSeriesModel
bool	DataIsLongitudinal ()

override bool	CheckDataValidity (object data, StringBuilder failMessage)
	This function checks to see if the object can be cast into the appropriate form for the model. More...

override void	OnDataConnection ()
	This function is called immediately after a data object is connected to the model. Any initial processing (e.g. determining dimension of parameter vector, etc.) should be done here. More...

Protected Member Functions inherited from CronoSeries.TimeSeries.Models.Model
abstract Vector< double >	ComputeConsequentialParameters (Vector< double > parameter)
	This function must fill in values of consequential parameters. These parameters are determined by the current ParameterState[] settings in ParameterStates. These can depend on the non-consequential parameters and the data set. The function should throw an exception if it is not possible. The parameter vector with parameters filled in should be returned. More...

double	NegativeLogLikelihood (Vector< double > partialCube)
	This function is a wrapper for another function, to be passed to a minimizer. More...

abstract bool	CheckParameterValidity (Vector< double > param)
	Checks for validity of parameters. More...

Static Protected Member Functions
static Complex	ComplexPower (Complex c, double pow)

Static Protected Member Functions inherited from CronoSeries.TimeSeries.Models.Model
static Vector< double >	CubeFix (Vector< double > cube)
	This function makes sure that its vector argument really does contain an element of the unit hypercube. If not, it is mapped back to an element on the hypercube (the mapping is continuous). More...

Protected Attributes
Data.TimeSeries	oneStepPredictors

Data.TimeSeries	oneStepPredictorsAtAvailability

Data.TimeSeries	oneStepPredStd

Data.TimeSeries	unstandardizedResiduals

Protected Attributes inherited from CronoSeries.TimeSeries.Models.UnivariateTimeSeriesModel
Longitudinal	longitudinalValues

Data.TimeSeries	values

Properties
override string	Description `[get]`

double	Mu `[get, set]`

double	Sigma `[get, set]`

Properties inherited from CronoSeries.TimeSeries.Models.TimeSeriesModel
override Vector< double >	Parameters `[get, set]`

Properties inherited from CronoSeries.TimeSeries.Models.Model
abstract string	Description `[get]`
	Should return a description of the model, including current parameter values if desired. More...

object	Residuals `[get, protected set]`

object	TheData `[get, set]`

double	GoodnessOfFit `[get, protected set]`

abstract Vector< double >	Parameters `[get, set]`

ParameterState[]	ParameterStates `[get, set]`

string	ToolTipText `[get, set]`

Properties inherited from CronoSeries.TimeSeries.Data.IConnectable
string	ToolTipText `[get, set]`

Additional Inherited Members
Public Types inherited from CronoSeries.TimeSeries.Models.Model
enum	ParameterState { Free, Locked, Consequential }

Public Attributes inherited from CronoSeries.TimeSeries.Models.Model
object	theData

Detailed Description

This is really like an ARMA model in many respects, but you specify the square root of the variance (sigma), and the autocorrelations at lags 1...maxLag, instead of specifying autoregressive and moving average polynomials. This means that we have to fall back on the Durbin-Levinson recursions for likelihood computations, instead of using more efficient recursions from Brockwell & Davis.

Member Function Documentation

◆ BuildForecasts()

override object CronoSeries.TimeSeries.Models.ACFModel.BuildForecasts	(	object	otherData,
		object	inputs
	)

inlinevirtual

This function generates forecasts (or fitted values) for the specified inputs, based on the existing data object and current model parameters. Results are returned in a form that depends on the model, for example, as a TimeSeries of DistributionSummary objects

Parameters

otherData
inputs	may have different interpretations, for time series, it is typically an IList of DateTime objects

Returns: a model-dependent object: for example, a time series of distribution summaries

Implements CronoSeries.TimeSeries.Models.Model.

◆ ComputeSpecialResiduals()

double [] CronoSeries.TimeSeries.Models.ACFModel.ComputeSpecialResiduals	(	Data.TimeSeries	startData,
		out double[]	rs,
		int	forecastHorizon,
		out double[]	forecasts
	)

inlineprotected

This function uses the Durbin-Levinson algorithm to get one-step predictors and residuals.

Parameters

rs	double[] array of one-step predictive MSEs / sigma^2, Returns double[] array of residuals = [ (x_i - \hat{x}_i)/\sqrt{rs_i} ]

◆ InitializeParameters()

override void CronoSeries.TimeSeries.Models.ACFModel.InitializeParameters ( )

inlineprotectedvirtual

This function is called after OnDataConnection. It can assume valid data is available, and it must fill in valid default parameter values. It typically also sets default parameter states, for purposes of estimation.

Implements CronoSeries.TimeSeries.Models.Model.

◆ SimulateData()

override Data.TimeSeries CronoSeries.TimeSeries.Models.ACFModel.SimulateData	(	List< DateTime >	dateTimes,
		int	randomSeed
	)

inlinevirtual

This function must simulate from the current model.

Parameters

dateTimes	these may have different interpretations, but for time series, is typically an IList of DateTime objects
randomSeed	random number seed

Returns

Implements CronoSeries.TimeSeries.Models.Model.

The documentation for this class was generated from the following file:

TimeSeries/Models/ACFModel.cs

Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Properties

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ BuildForecasts()

◆ ComputeSpecialResiduals()

◆ InitializeParameters()

◆ SimulateData()