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inst/ctre-app/dataPlot.md
In CTRE: Thresholding Bursty Time Series
Thresholding Bursty Time Series
The given datasets exhibit "bursty" behaviour:
- immediately after an even occurs, it's likely that there are more events
- sometimes there are extended periods in which no large events occur.
Try:
- varying the threshold height with the slider
- for simulated data, vary the
Tail parameter. It governs the
strength of burstiness (the closer to 0, the burstier; hit "Re-simulate"
after you change it).
- for magnitudes with a power-law distribution, such as the Solar Flare
data, a log scale is more appropriate
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CTRE documentation built on May 2, 2019, 9:34 a.m.
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Thresholding Bursty Time Series
The given datasets exhibit "bursty" behaviour:
- immediately after an even occurs, it's likely that there are more events
- sometimes there are extended periods in which no large events occur.
Try:
- varying the threshold height with the slider
- for simulated data, vary the
Tail parameter. It governs the strength of burstiness (the closer to 0, the burstier; hit "Re-simulate" after you change it). - for magnitudes with a power-law distribution, such as the Solar Flare data, a log scale is more appropriate
Try the CTRE package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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