No BSD License
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(X+X')/2;
end
% end lyap
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90*ceil(max(phase)/90);
try,...
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ARMLfit(rcs,ng,xg,rc0,lag_max...
The conditional-likelihood fit.
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ARShat_misd(ng,xg,L,lag_max,s...
ARSHAT_MISD AR models of increasing order from measurements with missing data
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ARhat_misd(ng,xg,rcinit,rc0,l...
ARHAT_MISD AR model from measurements with missing data
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ARselv(x,Lmax,M,settings)
ARselv Parameter estimation and order selection for vector AR models.
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KLDiscrepancy(ar_est,ma_est,v...
KLDiscrepancy Kullback-Leibler discrepancy.
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KLDiscrepancyv(varargin)
KLDiscrepancyv Kullback-Leibner discrepancy and index
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KLIndex(ar_est,ma_est,varx_es...
KLIndex Kullback-Leibler index.
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KLIndex_hat(ys,ar,ma,vary)
KLINDEX_HAT Estimate of the Kullback-Leibler index
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LikelihoodR(ys,ar0,ma0,var0,a...
LIKELIHOODR Likelihood ratio between estimated ARMA models
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YWoutput(cov,par)
function YW_out = YWoutput(cov,par)
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[ar,ma,ASAsellog,ASAcontrol]=...
ARH2ARMA ARMA model identification
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[ar,ma,ASAsellog,ASAcontrol]=...
ARMASEL_RS ARMAsel model identification
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[cor,gain,ASAcontrol]=arma2co...
ARMA2COR_E ARMA parameters to autocorrelations
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[mapar,var]=cor2b2(cor,maxite...
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[pc,R0,P,Pb]=burgv(x,Lmax)
BURGV
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ar2arset_e(varargin)
AR2ARSET_E AR parameters to optimal lower-order AR models
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ar_ma2rc(ar,ma,n_obs)
[Used internally by KLDiscrepancy, KLIndex, KLIndex_hat]
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arh2ar(varargin)
ARH2AR AR model identification
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arh2ma(varargin)
ARH2MA MA model identification
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armafilterv(x,a,b,prev_y);
ARMAFILTERV Digital ARMA filter for matrix-valued signals
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ch*z;
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chol(x)';
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cic(varargin)
CIC_S Finite sample order selection criterion for segments
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complex2real(xc)
COMPLEX2REAL Translates complex data into real data
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convol_e(varargin)
CONVOL_E Convolution sum
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convolrev_e(varargin)
CONVOLREV_E Convolution sum with one vector reversed
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convv(a,b);
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cov2pcv(cov)
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cumprod([var 1-rc(2:L+1).^2] ...
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dets(a)
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durbin2(ar_long,ar_ini,ma_ord...
function [ar_d2,ma_d2,pe_n] = durbin2(ar_long,ar_ini,ma_order,ASAcontrol)
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example_process(name,varargin...
EXAMPLE_PROCESS generates an example process
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gendata(a,b,n_obs,g,gar)
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gendatav(pc,R0,nobs)
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irr2grid(ti,xi,T);
IRR2GRID Conversion of irregularly sampled data to missing data
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iseven(x)
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likelihoodR_mod(ys,ar0,ar1,va...
LIKELIHOODR Likelihood ratio between estimated ARMA models
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ma2covv(ma,Peps,ncov)
function cov = ma2covv(ma,Peps,ncov)
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mat2sig(xmat);
MAT2SIG From matrix representation to signal representation
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matmat(linmap,siz,varargin)
MATMAT Matrix of a linear mapping from a matrix to a matrix.
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missing2seg(tg,xg)
MISSING2SEG Extracts segments from missing data.
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modeltype(ar,ma)
MODELTYPE provides a description for an ARMA-model
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moderr_e(varargin)
MODERR_E Model error
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moderrv(pchat,R0hat,pc,R0,nob...
moderrv Vector Model Error for AR models.
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par2covv(par,parb,R0)
-
par2pcv(par,parb,R0)
-
pc2arset(pc,R0,req_order)
PC2ARSET AR partial correlations to AR models
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pc2covv(pc,R0,ncov)
-
pc2parv(pc,R0)
-
pc2rcv(pc,R0)
-
pc2resv(pc,R0)
-
pc2specv(pc,R0,fn,T)
-
pc2xcovv(pc,R0,a,b,ncov)
-
pc2xspecv(pc,R0,a,b,fn,T)
-
pgain(ar,ma)
PGAIN Power gain of ARMA process [ar,ma]
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prodsumv(a,b);
-
psd2ar(h,p)
PSD2AR Power Spectral Density Function to AR parameters.
-
rc2parv(rc,rcb)
-
rc2pcv(rc,R0)
-
s(dim); end
-
setfields(struc_in,varargin)
function struc_out = setfields(struc_in,checkfields,varargin)
-
sig2ar_misd(ng,xg,L,varargin)...
SIG2AR_MISD Estimation of AR models from measurements with missing data.
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sig2mat(xsig);
SIG2MAT From signal representation to matrix representation.
-
spec2covv(h)
function cov = spec2covv(h)
-
sumARMA(varargin)
SUMARMA Sum of ARMA-processes
-
timesv(M,a);
-
traces(a)
-
transposeLTI(a)
function c = transposeLTI(a)
-
~nons;
-
Contents.m
-
Contents.m
-
Contents.m
-
Contents.m
-
KLI_demo.m
-
contents.m
-
data_segments.m
-
detection_demo.m
-
misdata_demo.m
-
prederrAR.m
-
redstat_demo.m
-
segments_demo.m
-
segments_unequal_demo.m
-
subband_demo.m
-
vectorsM_demo.m
-
vectors_demo.m
-
View all files
Automatic Spectral Analysis
by Stijn de Waele
03 Jul 2003
(Updated 09 May 2005)
Automatic spectral analysis for irregular sampling/missing data, analysis of spectral subband.
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Watch this File
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| File Information |
| Description |
Accurate estimates of the autocorrelation or power spectrum can be obtained with a parametric model (AR, MA or ARMA). With automatic inference, not only the model parameters but also the model structure are determined from the data. It is assumed that the ARMASA toolbox is present. This toolbox can be downloaded from the MATLAB Central file exchange at www.mathworks.com
The applications of this toolbox are:
- Reduced statistics ARMAsel: A compact yet accurate ARMA model is obtained based on a given power spectrum. Can be used for generation of colored noise with a prescribed spectrum.
- ARfil algorithm: The analysis of missing data/irregularly sampled signals
- Subband analysis: Accurate analysis of a part of the power spectrum
- Vector Autoregressive modeling: The automatic analysis of auto- and crosscorrelations and spectra
- Detection: Generally applicable test statistic to determine whether two signals have been generated by the same process or not. Based on the Kullback-Leibler index or Likelihood Ratio.
- Analysis of segments of data, possibly of unequal length.
For background information see my PhD thesis, available at http://www.dcsc.tudelft.nl/Research/PubSSC/thesis_sdewaele.html |
| MATLAB release |
MATLAB 7.0.1 (R14SP1)
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| Other requirements |
ARMASA toolbox (MatlabCentral) |
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| Comments and Ratings (11) |
| 10 Apr 2005 |
ZHONGCHAO HUANG
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| 20 Feb 2006 |
Trinh Quang Duc
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| 03 May 2006 |
zhou huilin
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| 26 Jul 2006 |
Zen Zeng
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sonny yanupraja
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Vishnuvenkatesh Dhage
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| 20 Feb 2008 |
syam Nagendla
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| Updates |
| 09 Jul 2003 |
minor modification |
| 09 Jul 2003 |
minor modification |
| 20 Aug 2003 |
durbin2 can be used separately |
| 09 May 2005 |
* included _e m-files for reduced statistics ARMAsel;
* made Matlab 7 proof;
* bug fix in KLDiscrepancy;
* new directory name. |
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Contact us at files@mathworks.com