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Functions

The following table lists functions in this documentation.

 
Name 
Description 
 
ACF 
Autocorrelation/autocovariance function. 
 
Fit ARAR algorithm. 
 
Forecast time series by ARAR. 
 
Burg AR estimation. 
 
Simulate the ARIMA process. 
 
Estimates autocorrelation/autocovariance function for the ARMA model. 
 
Forecast time series by using ARMA(p,q) model. 
 
Hannah-Rissanen ARMA estimation. 
 
Innovations ARMA estimation. 
 
Innovations ARMA estimation. 
 
ARMA process covariances. 
 
Calculate necessary covariances for ARMA(p,q) process up to kappa(KappaSize,KappaSize) 
 
-2log likelihood. 
 
Estimate ARMA process AR and MA coefficients. 
 
ARMA model one-step ahead predictors. 
 
Simulate the ARMA (p,q) process. 
 
Yule-Walker AR estimation. 
 
Autocovariance function. 
 
Box-Cox transformation. 
 
Inverse Box-Cox transformation. 
 
The box-Ljung statistics. 
 
Check AR(MA) coeefficients. 
 
This is function StatTimeSerAnalysis.CheckARMACoeffs. 
 
Double exponential forecast. 
 
First estimate Alpha and Gamma parameters by double smoothing and then use returned values to forecast up to T periods. 
 
Double exponential smoothing. 
 
In this case a fixed smoothing constants Alpha, Gamma are used in smoothing equations (no minimization is performed). 
 
The Durbin-Levinson algorithm. 
 
Calculates the Durbin-Watson statistic 
 
Uses the Innnovations algorithm to recursively calculate Theta[1,1]...Theta[n,n] coefficients (all coefficients). 
 
The innovations algorithm. 
 
This is function StatTimeSerAnalysis.InvTransformParams. 
 
Single moving average. 
 
PACF 
Partial autocorelation function. 
 
PACF 
Partial autocorrelation function. 
 
Memory-shortening filter. 
 
Single exponential forecast. 
 
rst estimate Alpha parameters by single smoothing and then use returned value to forecast up to T periods. 
 
Single exponential smoothing. 
 
In this case a fixed smoothing constant Alpha is used in smoothing equations (no minimization is performed). 
 
Setup initial values for integrating ARMA series. 
 
This is function StatTimeSerAnalysis.TransformParams. 
 
Triple exponential forecast. 
 
First estimate Alpha, Beta and Gamma parameters by triple exponential smoothing and then use returned values to forecast up to T periods. 
 
Triple exponential smoothing. 
 
In this case a fixed smoothing constants Alpha, Beta and Gamma are used in smoothing equations (no minimization is performed). 
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