CronoSeries.TimeSeries.Models Namespace Reference

Classes
class	ACFModel
	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...
class	ARMAModel
class	ARMAXModel
	This is just an ARMA model with exogenous inputs. The model is Phi(B) X_t = Theta(B) Z_t + Gamma U_{t-1} where Phi and Theta are standard autoregressive and moving average polynomials {U_t} is an exogenous time series. Much of the analysis is the same, we just have extra coefficients for the exogenous inputs, and to compute likelihoods, we have to adjust the observations appropriately. For now, we only allow a simple one-lag dependency on the exogenous series. Later I'll generalize this. More...
class	DistributionApproximation
	This class keeps track of a univariate distribution by storing its quantiles in a matrix. More...
class	DistributionSummary
class	DurbinLevinsonPredictor
	this class is used to carry out Durbin-Levinson recursions for linear time series prediction. it can use either a supplied vector containing the autocovariance, or a delegate that can compute it More...
class	GARCHModel
interface	IMLEEstimable
interface	IMoMEstimable
interface	IRealTimePredictable
class	Model
class	MVDistributionSummary
class	MVTimeSeriesModel
class	StandardOutputs
class	StateSpaceModel
class	TimeSeriesModel
class	UnivariateTimeSeriesModel
	This class represents a model for a univariate time series or for longitudinal data (a list of separate univariate time series) More...
class	VARModel
	vector autoregressive model, fittable by method of moments only (Yule-Walker eqns) More...

Functions
delegate double	AutocovarianceFunction (int lag)