R: inner correlation of occurrences (burstiness?)
I want to measure the inner correlation of the occurrences of events i.e. I want to distinguish between the two (drawn) [samples (follow the link for the sketches I have not yet enough reputation to post pictures)][1] and say "in the second sample events occur more conglomerate compared to the first".
Isn't this some kind of burstiness?

I have [drawn][1] a possible representation of the expected result (for S2) in the image below the samples (just as it is in my mind now), but don't hesitate to suggest different proposals.
 
I looked out and tried different build-in functions and googled for R and burstiness, but I didn't make progress. It further seemed to me (by reading papers about burstiness) that there is no common measure for burstiness and it would be cool to justify why choosing a specific one.

This is how the time series is represented, currently:


     [1]  3.256861  3.377142  3.941173  4.304236  4.485358  4.606512  4.707296
     [8]  5.473004  5.714746  5.815394  5.835405  5.936067  5.957008  6.964611
    [15]  7.045158  7.065171  7.165824  7.669618  8.173240  8.273692  9.503988
    [22]  9.604991  9.624853  9.725522 10.237766 10.954529 11.378399 12.687714
    [29] 13.291919 13.412580 13.675270 14.380529 14.743638 15.247138 15.851832
    [36] 15.952875 15.972497 16.456259 16.476052 17.201506 17.463708 18.068535
    [43] 18.309645 18.390292 18.410299 18.430323 18.531736 18.652921 18.793662
    [50] 19.297076 19.639692 19.760698 20.768096 20.868441 20.990499 21.494412
    [57] 21.856368 22.199341 22.219143 22.440472 22.481118 23.327013 23.447678
    [64] 23.811188 23.843000 24.113302

(Should I upload a corresponding csv?)


  [1]: http://dat-berger.de/data/burstiness.png
R: inner correlation of occurrences (burstiness?)
===

I want to measure the inner correlation of the occurrences of events i.e. I want to distinguish between the two (drawn) [samples (follow the link for the sketches I have not yet enough reputation to post pictures)][1] and say "in the second sample events occur more conglomerate compared to the first".
Isn't this some kind of burstiness?

I have [drawn][1] a possible representation of the expected result (for S2) in the image below the samples (just as it is in my mind now), but don't hesitate to suggest different proposals.
 
I looked out and tried different build-in functions and googled for R and burstiness, but I didn't make progress. It further seemed to me (by reading papers about burstiness) that there is no common measure for burstiness and it would be cool to justify why choosing a specific one.

This is how the time series is represented, currently:


     [1]  3.256861  3.377142  3.941173  4.304236  4.485358  4.606512  4.707296
     [8]  5.473004  5.714746  5.815394  5.835405  5.936067  5.957008  6.964611
    [15]  7.045158  7.065171  7.165824  7.669618  8.173240  8.273692  9.503988
    [22]  9.604991  9.624853  9.725522 10.237766 10.954529 11.378399 12.687714
    [29] 13.291919 13.412580 13.675270 14.380529 14.743638 15.247138 15.851832
    [36] 15.952875 15.972497 16.456259 16.476052 17.201506 17.463708 18.068535
    [43] 18.309645 18.390292 18.410299 18.430323 18.531736 18.652921 18.793662
    [50] 19.297076 19.639692 19.760698 20.768096 20.868441 20.990499 21.494412
    [57] 21.856368 22.199341 22.219143 22.440472 22.481118 23.327013 23.447678
    [64] 23.811188 23.843000 24.113302

(Should I upload a corresponding csv?)


  [1]: http://dat-berger.de/data/burstiness.png