文件列表:

ARMAsel_mis_irreg (0, 2009-03-20)
ARMAsel_mis_irreg\ARfil.m (300, 2008-05-26)
ARMAsel_mis_irreg\ARfil_irreg.m (327, 2005-02-23)
ARMAsel_mis_irreg\ARjones.m (286, 2008-08-26)
ARMAsel_mis_irreg\ARJones_irreg.m (325, 2004-07-06)
ARMAsel_mis_irreg\ARMAsel_irreg.m (8117, 2008-11-26)
ARMAsel_mis_irreg\ARMAsel_mis.m (6734, 2008-08-26)
ARMAsel_mis_irreg\ARMA_MLfit.m (2695, 2004-11-12)
ARMAsel_mis_irreg\ARMLfit_irreg.m (2503, 2007-10-18)
ARMAsel_mis_irreg\irreg_data.mat (1840, 2008-08-27)
ARMAsel_mis_irreg\irreg_demo.m (3787, 2009-05-21)
ARMAsel_mis_irreg\Jonesfit.m (3427, 2008-08-25)
ARMAsel_mis_irreg\Jonesfit_irreg.m (4448, 2008-09-30)
ARMAsel_mis_irreg\keepdata.m (695, 2007-11-23)
ARMAsel_mis_irreg\mis_demo.m (7875, 2008-09-30)
ARMAsel_mis_irreg\mssnnr.m (1627, 2006-10-04)
ARMAsel_mis_irreg\nnresample.m (2133, 2008-11-13)
ARMAsel_mis_irreg\simple_irreg_demo.m (5106, 2009-05-21)
ARMAsel_mis_irreg\simple_mis_demo.m (2721, 2008-01-24)
license.txt (1338, 2009-05-25)

ARMAsel_mis_irreg Toolbox for use with Matlab ============================================= Title to ownership ------------------ Any part of the ARMAsel_mis_irreg software package may freely be used for scientific or educational purposes. For commercial applications, permission is required from P.M.T. Broersen (address stated below). How to Install -------------- Unzip the file ARMAsel_mis_irreg.ZIP to a directory where you want it to reside. You will need the ARMASA toolbox of Piet Broersen and several files of the Automatic Spectal Analysis toolbox of Stijn de Waele. Both are available at http://www.mathworks.com/matlabcentral/fileexchange in ... signal processing ... spectral analysis ARMAsel_mis_irreg uses the commercial MATLAB OPTIM toolbox for optimization. Relation with the missing and irregular data software of Stijn de Waele ----------------------------------------------------------------------- The software of Stijn de Waele is the basis of ARMAsel_mis_irreg. The candidate model types MA and ARMA have been added. A new algorithm for AR likelihood computation has been added that gives the same results for AR models, but the new software can sometimes be faster. Multi shift slotted nearest neighbor resampling is introduced to replace an irregularly sampled signal by several equidistant signals. The slot width is made smaller than the resampling distance to diminish the bias caused by shifting the irregular observation times to an equidistant grid. User support ------------ An extended list of references is given in the ARMASA infotxt file of the ARMASA toolbox. Open Journal paper, for free download: Broersen, P.M.T. (2008). Spectral Analysis of Irregularly Sampled Data with Time Series Models. Bentham Open, The Open Signal Processing Journal, vol. 1, doi: 10.2174/1876825300801010007, pp. 7-14. http://bentham.org/open/tosigpj/index.htm Book with background of spectral analysis : Piet M.T. Broersen Automatic Autocorrelation and Spectral Analysis Springer-Verlag,London, 2006. ISBN 1-84628-328. Journal paper about ARMAsel : P. M. T. Broersen, Automatic Spectral Analysis with Time Series Models, IEEE Transactions on Instrumentation and Measurement, Vol. 51, No. 2, April 2002, pp. 211-216. Journal papers about ARMAsel_mis for missing data problems : Broersen P.M.T., S. de Waele and R. Bos Application of Autoregressive Spectral Analysis to Missing Data Problems. IEEE Trans. on Instrumentation and Measurement, vol. 53, no. 4, p ***1-***6, 2004. Broersen P.M.T., S. de Waele and R. Bos Autoregressive Spectral Analysis when Observations are Missing. Automatica, vol. 40, p 1495-1504, 2004. Broersen, P.M.T. and R. Bos Time Series Analysis if Data Are Randomly Missing. IEEE Trans. on Instrumentation and Measurement, vol. 55, no.1, p 79-84, 2006. Broersen, P.M.T. Automatic Spectral Analysis with Missing Data Digital Signal Processing, vol. 16, p 754-766, 2006. Broersen, P.M.T. Who is Afraid of Missing Data in Spectral Analysis. (invited software paper) Preprints SYSID 2006, 14th IFAC Symp. on System Identification, Newcastle, Australia, p 702-707, 2006. Journal papers about ARMAsel_irreg for irregularly sampled problems : Broersen, P.M.T. and R. Bos Estimating Time Series Models from Irregularly Spaced Data. IEEE Trans. on Instrumentation and Measurement, vol. 55 no. 4, p 1124-1131, 2006. Broersen, P.M.T. Time Series Models for Spectral Analysis of Irregular Data Far Beyond the Mean Data Rate Measurement Science Technology, vol. 19, no 015103, p 1-14, 2008. The program that runs by typing "simple_mis_demo" in MATLAB gives a demonstration of how the program can be applied most easily, with only AR candidate models and without any additional information. Only the parameters of the selected AR model are on the output. The program mis_demo has AR, MA and ARMA candidate models, between which one single model is selected automatically. Furthermore, it demonstrates how additional information about the models that have not been selected is obtained by adding an additional output variable. It gives the possibility to verify most results in the Digital Signal Processing paper. *** Typing : [asel,bsel]=ARMAsel_mis(ti,xi,ARmax); psdsel=arma2psd(asel,bsel,500); corsel=arma2cor(asel,bsel,20); gives 500 points of the estimated spectrum and 20 points of the autocorrelation function. The inputs ti and xi are row vectors. ti are integer numbers as observation times xi are the observations at ti ARmax is the maximum AR candidate order. The program that runs by typing "simple_irreg_demo" in MATLAB gives a demonstration of how the program for irregular data can be applied most easily without any additional information. The program irreg_demo shows some additional possibilities. *** Typing : [air,bir,sellogir]=ARMAsel_mis(ti,xi,ARmax,Tr,w); gives the parameters of the selected model with additional information in sellogir. Tr is the equidistant resampling time distance, chosen by the data analyst w is the slot width, as a fraction of Tr; w = 1/2 or 1/4 is often a good choice. Any questions on the theoretical foundations and the applicability of the functions can be submitted to: p.m.t.broersen@tudelft.nl or p.broersen@xs4all.nl P.M.T. Broersen Delft University of Technology Faculty of Multi Scale Physics Prins Bernhardlaan 6 2628 BW Delft the Netherlands email: p.m.t.broersen@tudelft.nl p.broersen@xs4all.nl

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