RECURRENCE PLOTS

Hi, no, this is not correct. We have fixed the control parameter a as I wrote and then calculated a time series for this one value of a . For this time series we calculated one recurrence plot and from this recurrence plot the RQA measures. The value of a was not changed here. It is the same. Thus, ...

Hi, the difference between the two papers is that in the first one the parameter a was slightly changed with each iteration step, where in the other paper for each value of a a separate time series was calculated and analysed by RQA. The initial value should not be so important. You could start, e.g...

I wonder, do we get the same recurrence plots for both original and the normalized series? How about the quantification measures? Are there any references for this? Thank you. As long as you rescale the recurrence threshold you can get exactly the same recurrence plot. Similar issues are explained ...

1) Assuming the embedding dimension is m, i will get m-1 adjacency matrices ( for dimension 2 to m ). I am interesting in computing some network measures like clustering coefficient. Do I compute this measure for each dimension and then average it ? Or how do it take all the dimension into consider...

Here is the answer:
http://tocsy.pik-potsdam.de/CRPtoolbox/ ... l#download

Are you perhaps using a proxy which is modifying the download file? It seems that the install file was somehow modified during file transfer.

Hi, I would do it like this % calculate the RQA measures w = 200; ws = 100; R = crqa(X,1,2,3,w,ws); % open results file for writing fid = fopen('resultsfile.dat','w'); % write the filenames in the file fprintf(fid,'file X: %s\nfile Y: %s\n',X,Y); % write the head line (variable names) fprintf(fid,'\...

I'm a bit confused by mixing filename and the vectors X and Y. What do your really would like to do? Just to save the original time series along the RQA measures?

Hi,

waht do you mean with many samples? If you ask for the maximal length of time series which can be used in CRP toolbox, just read the FAQ:
http://tocsy.pik-potsdam.de/CRPtoolbox/ ... tml#length

Please have a look at
Kennel, M. B., Brown, R., Abarbanel, H. D. I.: Determining embedding dimension for phase-space reconstruction using a geometrical construction, Phys. Rev. A, 45, 1992.

Hello, the current version of twinsurr does not allow the use of multi-column vectors. It can only be used with single-column vectors which can be embedded in higher dimensions by using the parameters of this function. Moreover, I have to e,phasise that twinsurr is not identical with the published a...

Hi, the answer is simple. Because of the long time, a Matlab code will need to calculate the false nearest neighbours, not all points are used, but instead only a subset of 200 points. These points are randomly chosen, therefore, causing slightly different results when calling this function several ...

Hi, I do not understand completely your problem. What are you doing when using the loop? Are you dividing the original time series into several parts and then trying to apply the RQA on these sub-parts? Have you tried to apply RQA individually on one of such sub-parts? What is the dimension (size) o...

hello angelo, i would say that the definitions of diagonal and vertical lines in a RP are quite clear and well explained in several publications, e.g., in N. Marwan, M. C. Romano, M. Thiel, J. Kurths: Recurrence Plots for the Analysis of Complex Systems, Physics Reports, 438(5-6), 237-329, DOI:10.10...

Dear friends of recurrence plots and recurrence quantification! The next symposium will be held in the city in which recurrence quantification have been developed: in Chicago! The event will be at 14-16 August 2013. More information can be found at the symposium web site . We look foward to meet you...

hi, we have a time series x . you have now to calculate the mutual information MI between the x(t) and a time delayed version of this time series x(t+tau) . this is analogous to the auto-correlation. then plot the MI versus the delay tau . usually, at the beginning, you will find a steep decrease wi...