I converted some EEG time series to their related visibility graphs, now I need to find correlation between each 2 graphs, please note that there is no any interlink between each 2 graphs hence I'm not sure if it is possible to use some synchronization methods of complex networks.
You can use cross-correlation. It reminds convolution, but it more suitable to 'real-data' graphs. make sure that you use implementation that reduce the base line (the average) and divides by the Std to be able to interpret the results
It depends. Are the periods iqual? If the time series has some similar trend, then its better to remove the trend at first. Are the time series stationary? Did you make an unit root test? I think it is appropriate to make also a co-integration test, in case they are not stationary.
Alternatively, Can you assume that each pairwise pointset contains random members, then demonstrate that they are not statistically random? That behavior should lead you to an appropriate model.
Dear Celso
thank you for your answer, time series are not periodic, actually we extracted some complex time series
Dear Todd
thanks a lot, which model do you mean? I didn't get your mean correctly
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