1 % Time Series Analysis - A toolbox for the use with Matlab and Octave.
3 % $Id: contents.m 5090 2008-06-05 08:12:04Z schloegl $
4 % Copyright (C) 1996-2004,2008 by Alois Schloegl <a.schloegl@ieee.org>
\r% WWW: http://hci.tugraz.at/~schloegl/matlab/tsa/
6 % This program is free software: you can redistribute it and/or modify
7 % it under the terms of the GNU General Public License as published by
8 % the Free Software Foundation, either version 3 of the License, or
9 % (at your option) any later version.
11 % This program is distributed in the hope that it will be useful,
12 % but WITHOUT ANY WARRANTY; without even the implied warranty of
13 % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
14 % GNU General Public License for more details.
16 % You should have received a copy of the GNU General Public License
17 % along with this program. If not, see <http://www.gnu.org/licenses/>.
20 % Time Series Analysis - a toolbox for the use with Matlab
21 % aar adaptive autoregressive estimator
22 % acovf (*) Autocovariance function
23 % acorf (acf) (*) autocorrelation function
24 % pacf (*) partial autocorrelation function, includes signifcance test and confidence interval
25 % parcor (*) partial autocorrelation function
26 % biacovf biautocovariance function (3rd order cumulant)
28 % durlev (*) solves Yule-Walker equation - converts ACOVF into AR parameters
29 % lattice (*) calcultes AR parameters with lattice method
30 % lpc (*) calculates the prediction coefficients form a given time series
31 % invest0 (*) a prior investigation (used by invest1)
32 % invest1 (*) investigates signal (useful for 1st evaluation of the data)
33 % rmle AR estimation using recursive maximum likelihood function
34 % selmo (*) Select Order of Autoregressive model using different criteria
36 % hup (*) test Hurwitz polynomials
37 % ucp (*) test Unit Circle Polynomials
38 % y2res (*) computes mean, variance, skewness, kurtosis, entropy, etc. from data series
39 % ar_spa (*) spectral analysis based on the autoregressive model
40 % detrend (*) removes trend, can handle missing values, non-equidistant sampled data
41 % flix floating index, interpolates data for non-interger indices
44 % Multivariate analysis
45 % adim adaptive information matrix (inverse correlation matrix)
46 % mvar multivariate (vector) autoregressive estimation
47 % mvaar multivariate adaptvie autoregressive estimation using Kalman filtering
48 % mvfilter multivariate filter
49 % mvfreqz multivariate spectra
50 % arfit2 provides compatibility to ARFIT [Schneider and Neumaier, 2001]
53 % Conversions between Autocorrelation (AC), Autoregressive parameters (AR),
54 % prediction polynom (POLY) and Reflection coefficient (RC)
55 % ac2poly (*) transforms autocorrelation into prediction polynom
56 % ac2rc (*) transforms autocorrelation into reflexion coefficients
57 % ar2rc (*) transforms autoregressive parameters into reflection coefficients
58 % rc2ar (*) transforms reflection coefficients into autoregressive parameters
59 % poly2ac (*) transforms polynom to autocorrelation
60 % poly2ar (*) transforms polynom to AR
67 % sinvest1 shows the parameter calculated by INVEST1
70 % tsademo demonstrates INVEST1 on EEG data
71 % invfdemo demonstration of matched, inverse filtering
72 % bisdemo demonstrates bispectral estimation
74 % (*) indicates univariate analysis of multiple data series (each in a row) can be processed.
75 % (-) indicates that these functions will be removed in future
77 % REFERENCES (sources):
78 % http://www.itl.nist.gov/
79 % http://mathworld.wolfram.com/
80 % P.J. Brockwell and R.A. Davis "Time Series: Theory and Methods", 2nd ed. Springer, 1991.
81 % O. Foellinger "Lineare Abtastsysteme", Oldenburg Verlag, Muenchen, 1986.
82 % F. Gausch "Systemtechnik", Textbook, University of Technology Graz, 1993.
83 % M.S. Grewal and A.P. Andrews "Kalman Filtering" Prentice Hall, 1993.
84 % S. Haykin "Adaptive Filter Theory" 3ed. Prentice Hall, 1996.
85 % E.I. Jury "Theory and Application of the z-Transform Method", Robert E. Krieger Publishing Co., 1973.
86 % M.S. Kay "Modern Spectal Estimation" Prentice Hall, 1988.
87 % Ch. Langraf and G. Schneider "Elemente der Regeltechnik", Springer Verlag, 1970.
88 % S.L. Marple "Digital Spetral Analysis with Applications" Prentice Hall, 1987.
89 % C.L. Nikias and A.P. Petropulu "Higher-Order Spectra Analysis" Prentice Hall, 1993.
90 % M.B. Priestley "Spectral Analysis and Time Series" Academic Press, 1981.
91 % T. Schneider and A. Neumaier "Algorithm 808: ARFIT - a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models"
92 % ACM Transactions on Mathematical software, 27(Mar), 58-65.
93 % C.E. Shannon and W. Weaver "The mathematical theory of communication" University of Illinois Press, Urbana 1949 (reprint 1963).
94 % W.S. Wei "Time Series Analysis" Addison Wesley, 1990.
97 % REFERENCES (applications):
98 % [1] A. Schlögl, B. Kemp, T. Penzel, D. Kunz, S.-L. Himanen,A. Värri, G. Dorffner, G. Pfurtscheller.
99 % Quality Control of polysomnographic Sleep Data by Histogram and Entropy Analysis.
100 % Clin. Neurophysiol. 1999, Dec; 110(12): 2165 - 2170.
101 % [2] Penzel T, Kemp B, Klösch G, Schlögl A, Hasan J, Varri A, Korhonen I.
102 % Acquisition of biomedical signals databases
103 % IEEE Engineering in Medicine and Biology Magazine 2001, 20(3): 25-32
104 % [3] Alois Schlögl (2000)
105 % The electroencephalogram and the adaptive autoregressive model: theory and applications
106 % Shaker Verlag, Aachen, Germany,(ISBN3-8265-7640-3).
109 % - Multiple Signal Processing
110 % - Efficient algorithms
111 % - Model order selection tools
112 % - higher (3rd) order analysis
113 % - Maximum entropy spectral estimation
114 % - can deal with missing values (NaN's)