Calculates adaptive autoregressive (AAR) and adaptive autoregressive moving average estimates (AARMA) of real-valued data series using Kalman filter algorithm.
Estimating Adaptive AutoRegressive-Moving-Average-and-mean model (includes mean term)
converts the autocorrelation sequence into an AR polynomial
converts the autocorrelation function into reflection coefficients
Calculates autocorrelations for multiple data series.
estimates autocovariance function (not normalized) NaN's are interpreted as missing values.
adaptive information matrix.
Adaptive Mean-AutoRegressive-Moving-Average model estimation
converts autoregressive parameters into AR polymials Multiple polynomials can be converted.
converts autoregressive parameters into reflection coefficients
decomposes an AR-spectrum into its compontents
extracts AR and RC of order P from Matrix MX
estimates multivariate autoregressive parameters
BiAutoCovariance function
Shows BISPECTRUM of eeg8s.mat
Calculates Bispectrum
removes the trend from data, NaN's are considered as missing values
estimates AR(p) model parameter by solving the
The use of FLAG_IMPLICIT_SAMPLERATE is in experimental state.
floating point index - interpolates data in case of non-integer indices
calculates histogram for each column
calculates histogram of each column
calculates histogram and performs data compression
calculates histogram for rows and supports data compression
tests if the polynomial C is a Hurwitz-Polynomial.
First Investigation of a signal (time series) - automated part
First Investigation of a signal (time series) - interactive
demonstrates Inverse Filtering
Estimates AR(p) model parameter with lattice algorithm (Burg 1968) for multiple channels.
Linear prediction coefficients
adaptive AR estimation base on a multidimensional Kalman filer algorithm.
estimates Multi-Variate AutoRegressive model parameters
Multi-variate filter function
multivariate frequency response
Partial Autocorrelation function
estimates partial autocorrelation coefficients Multiple channels can be used (one per row).
converts an AR polynomial into an autocorrelation sequence
Converts AR polymials into autoregressive parameters.
converts AR-polynomial into reflection coefficients
converts reflection coefficients to autocorrelation sequence
converts reflection coefficients into autoregressive parameters
converts reflection coefficients into an AR-polynomial
estimates AR Parameters using the Recursive Maximum Likelihood
show BISPECTRUM
Model order selection of an autoregrssive model
model order selection for univariate and multivariate
shows the parameters of a time series calculated by INVEST1
demonstrates INVEST1 on EEG data
tests if the polynomial C is a Unit-Circle-Polynomial.
evaluates basic statistics of a data series
