Thresholding Bursty Time Series

Models extremes of 'bursty' time series via Continuous Time Random Exceedances (CTRE). See , K. Hees, S. Nayak, P.Straka, 2018.


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This R package provides tools to analyse extremes of ‘bursty’ time series. Burstiness is characterized by heavy-tailed inter-arrival times and scale-free event dynamics. The CTRE model captures burstiness by generalizing the Poisson process to a fractional Poisson process, with Mittag-Leffler inter-arrival times. Parameter estimates are read off from stability plots, and goodness of fit is assessed via diagnostic plots; see the Shiny app below.

Reference

“Peaks Over Threshold for Bursty Time Series”, Katharina Hees, Smarak Nayak, Peter Straka (2018). https://arxiv.org/abs/1802.05218

Shiny App

The package comes with two examples of bursty time series: solar flare magnitudes and bitcoin trading volumes. For parameter estimates of the Mittag-Leffler distribution, see the tab “Exceedance Times”. CTRE model assumptions are checked via a QQ plot of the Mittag-Leffler distribution; an empirical copula plot checking for dependence between inter-arrival times and magnitudes; and a plot of the autocorrelation function for the two series (interarrival times and magnitudes). For the standard POT model plots, see the “Exceedances” tab.

Install from GitHub

library("devtools")
install_github("UNSW-MATH/CTRE")
library(CTRE)

Run shiny app

You can run the above Shiny app from within RStudio:

runCTREshiny()

Package usage

You can

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Reference manual

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install.packages("CTRE")

0.1.0 by Peter Straka, a year ago


https://unsw-math.github.io/CTRE/


Report a bug at https://github.com/UNSW-MATH/CTRE/issues


Browse source code at https://github.com/cran/CTRE


Authors: Katharina Hees [aut] , Peter Straka [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports assertthat, graphics, MittagLeffleR, plyr, stats, tea, utils, zoo

Suggests evmix, magrittr, shiny


See at CRAN