Method of finding neighbours.

General discussions and questions about recurrence plot related methods.

Method of finding neighbours.

Postby sudhaeinstein » Wed Jan 7, 2015 10:52

HI,

I have been using CRP tool box for over a month. I would like to have more references on when to use what type of method to find neighbors.

For instance there is max norm euclidean min norm rr fan etc. Given the source of data ( ex EEG, Population of a species over a time, gamma radiation from earth, daily temperature )
EEG is riddled with noise, gamma radiation is almost a constant except for few fluctuations, population data can be considered almost accurate, daily temperature may show seasonal variation and effects of global warming.

If one uses different threshold one gets a different plot. So how do we make predictions.


Thanks in advance.
Kindly excuse me if it is too basic or too advanced question.

Best regards

Sudharsana V I
sudhaeinstein
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Re: Method of finding neighbours.

Postby David » Mon Jun 22, 2015 15:49

Hi,

there is a nice review paper by Marwan et al. which explains the different types of norms: Marwan, N., Carmenromano, M., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5-6), 237–329. doi:10.1016/j.physrep.2006.11.001

Regarding RP and EEG there is some research e.g. by Marwan, Schinkel etc. which indicates that the Order Pattern RP is a good choice, since it is robust against noise and non-stationarity, see the following list of publications for example:

Schinkel, S., Marwan, N., & Kurths, J. (2007). Order patterns recurrence plots in the analysis of ERP data. Cognitive Neurodynamics, 1(4), 317–325. doi:10.1007/s11571-007-9023-z
Schinkel, S., Marwan, N., & Kurths, J. (2009). Brain signal analysis based on recurrences. Journal of Physiology Paris, 103(6), 315–323. doi:10.1016/j.jphysparis.2009.05.007
Schinkel, S., Zamora-López, G., Dimigen, O., Sommer, W., & Kurths, J. (2012). Order Patterns Networks (ORPAN)-a method to estimate time-evolving functional connectivity from multivariate time series. Frontiers in Computational Neuroscience, 6(November), 91. doi:10.3389/fncom.2012.00091

greetings

David
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