Time Series Analysis (Ver 3.10)
Schloegl A. (1996-2003,2008) Time Series Analysis - A Toolbox for the use with Matlab.
WWW: http://pub.ist.ac.at/~schloegl/matlab/tsa/
$Id: content.m 5090 2008-06-05 08:12:04Z schloegl $
Copyright (C) 1996-2003,2008 by Alois Schloegl <a.schloegl@ieee.org>
Time Series Analysis - a toolbox for the use with Matlab
aar adaptive autoregressive estimator
acovf (*) Autocovariance function
acorf (acf) (*) autocorrelation function
pacf (*) partial autocorrelation function, includes signifcance test and confidence interval
parcor (*) partial autocorrelation function
biacovf biautocovariance function (3rd order cumulant)
bispec Bi-spectrum
durlev (*) solves Yule-Walker equation - converts ACOVF into AR parameters
lattice (*) calcultes AR parameters with lattice method
lpc (*) calculates the prediction coefficients form a given time series
invest0 (*) a prior investigation (used by invest1)
invest1 (*) investigates signal (useful for 1st evaluation of the data)
selmo (*) Select Order of Autoregressive model using different criteria
histo (*) histogram
hup (*) test Hurwitz polynomials
ucp (*) test Unit Circle Polynomials
y2res (*) computes mean, variance, skewness, kurtosis, entropy, etc. from data series
ar_spa (*) spectral analysis based on the autoregressive model
detrend (*) removes trend, can handle missing values, non-equidistant sampled data
flix floating index, interpolates data for non-interger indices
quantiles calculates quantiles
Multivariate analysis (planned in future)
mvar multivariate (vector) autoregressive estimation
mvfilter multivariate filter
arfit2 provides compatibility to ARFIT [Schneider and Neumaier, 2001]