List of Functions for the 'tsa' package

tsa

Stochastic concepts and maximum entropy methods for time series analysis

Select category:

Univariate (stationary) analysis

acovf

ACOVF estimates autocovariance function (not normalized) NaN's are interpreted as missing values.

acorf

Calculates autocorrelations for multiple data series.

biacovf

BiAutoCovariance function [BiACF] = biacovf(Z,N);

bispec

Calculates Bispectrum [BISPEC] = bispec(Z,N);

durlev

function [AR,RC,PE] = durlev(ACF); function [MX,PE] = durlev(ACF); estimates AR(p) model parameter by solving the Yule-Walker with the Durbin-Levinson recursion for multiple channels INPUT:

lattice

Estimates AR(p) model parameter with lattice algorithm (Burg 1968) for multiple channels.

rmle

RMLE estimates AR Parameters using the Recursive Maximum Likelihood Estimator according to [1] Use: [a,VAR]=rmle(x,p) Input: x is a column vector of data p is the model order Output: a is

pacf

Partial Autocorrelation function [parcor,sig,cil,ciu] = pacf(Z,N);

parcor

estimates partial autocorrelation coefficients Multiple channels can be used (one per row).

invest0

First Investigation of a signal (time series) - automated part [AutoCov,AutoCorr,ARPMX,E,ACFsd,NC]=invest0(Y,Pmax);

invest1

First Investigation of a signal (time series) - interactive [AutoCov,AutoCorr,ARPMX,E,CRITERIA,MOPS]=invest1(Y,Pmax,show);

selmo

Model order selection of an autoregrssive model [FPE,AIC,BIC,SBC,MDL,CAT,PHI,optFPE,optAIC,optBIC,optSBC,optMDL,optCAT,optPHI]=selmo(E,N);

selmo2

SELMO2 - model order selection for univariate and multivariate autoregressive models X = selmo(y,Pmax); y data series Pmax maximum model order X.A, X.B, X.C parameters of AR mode

hup

HUP(C) tests if the polynomial C is a Hurwitz-Polynomial.

ucp

UCP(C) tests if the polynomial C is a Unit-Circle-Polynomial.

y2res

Y2RES evaluates basic statistics of a data series R = y2res(y) several statistics are estimated from each column of y OUTPUT: R.N number of samples, NaNs are not counted R.SUM sum

ar_spa

AR_SPA decomposes an AR-spectrum into its compontents [w,A,B,R,P,F,ip] = ar_spa(AR,fs,E);

flix

floating point index - interpolates data in case of non-integer indices

Multivariate stationary analysis

mvar

MVAR estimates parameters of the Multi-Variate AutoRegressive model

mvfilter

Multi-variate filter function

mvfreqz

MVFREQZ multivariate frequency response [S,h,PDC,COH,DTF,DC,pCOH,dDTF,ffDTF,pCOH2,PDCF,coh,GGC,Af,GPDC,GGC2,DCOH] = mvfreqz(B,A,C,f,Fs) [...] = mvfreqz(B,A,C,N,Fs) INPUT: ======= A, B mult

arfit2

ARFIT2 estimates multivariate autoregressive parameters of the MVAR process Y

Adaptive (time-varying) analysis

aar

Calculates adaptive autoregressive (AAR) and adaptive autoregressive moving average estimates (AARMA) of real-valued data series using Kalman filter algorithm.

aarmam

Estimating Adaptive AutoRegressive-Moving-Average-and-mean model (includes mean term)

adim

ADIM adaptive information matrix.

amarma

Adaptive Mean-AutoRegressive-Moving-Average model estimation [z,e,ESU,REV,V,Z,SPUR] = amarma(y, mode, MOP, UC, z0, Z0, V0, W);

mvaar

Multivariate (Vector) adaptive AR estimation base on a multidimensional Kalman filer algorithm.

Conversions between forms

ac2poly

converts the autocorrelation sequence into an AR polynomial [A,Efinal] = ac2poly(r)

ac2rc

converts the autocorrelation function into reflection coefficients [RC,r0] = ac2rc(r)

ar2rc

converts autoregressive parameters into reflection coefficients with the Durbin-Levinson recursion for multiple channels function [AR,RC,PE] = ar2rc(AR); function [MX,PE] = ar2rc(AR);

rc2ar

converts reflection coefficients into autoregressive parameters uses the Durbin-Levinson recursion for multiple channels function [AR,RC,PE,ACF] = rc2ar(RC); function [MX,PE] = rc2ar(RC);

poly2ac

converts an AR polynomial into an autocorrelation sequence [R] = poly2ac(a [,efinal] );

poly2ar

Converts AR polymials into autoregressive parameters.

poly2rc

converts AR-polynomial into reflection coefficients [RC,R0] = poly2rc(A [,Efinal])

rc2ac

converts reflection coefficients to autocorrelation sequence [R] = rc2ac(RC,R0);

rc2poly

converts reflection coefficients into an AR-polynomial [a,efinal] = rc2poly(K)

ar2poly

converts autoregressive parameters into AR polymials Multiple polynomials can be converted.

Utility functions

arcext

ARCEXT extracts AR and RC of order P from Matrix MX function [AR,RC] = arcext(MX,P);

sinvest1

SINVEST1 shows the parameters of a time series calculated by INVEST1 only called by INVEST1

sbispec

SBISPEC show BISPECTRUM

flag_implicit_samplerate

The use of FLAG_IMPLICIT_SAMPLERATE is in experimental state.

Test suites

tsademo

TSADEMO demonstrates INVEST1 on EEG data

bisdemo

BISDEMO (script) Shows BISPECTRUM of eeg8s.mat

invfdemo

invfdemo demonstrates Inverse Filtering

Package: tsa