| autoSpec {astsa} | R Documentation |
autoSpec - Changepoint Detection of Narrowband Frequency Changes
Description
Uses changepoint detection to discover if there have been slight changes in frequency in a time series. The autoSpec procedure uses minimum description length (MDL) to do nonparametric spectral estimation with the goal of detecting changepoints. Optimization is accomplished via a genetic algorithm (GA).
Usage
autoSpec(xdata, Pi.B = NULL, Pi.C = NULL, PopSize = 70, generation = 70, m0 = 10,
Pi.P = 0.3, Pi.N = 0.3, NI = 7, taper = .5, min.freq = 0, max.freq = .5)
Arguments
xdata |
time series (of length n at least 100) to be analyzed; the |
Pi.B |
probability of being a breakpoint in initial stage; default is 10/n. Does not need to be specified. |
Pi.C |
probability of conducting crossover; default is (n-10)/n. Does not need to be specified. |
PopSize |
population size (default is 70); the number of chromosomes in each generation. Does not need to be specified. |
generation |
number of iterations; default is 70. Does not need to be specified. |
m0 |
maximum width of the Bartlett kernel is |
Pi.P |
probability of taking parent's gene in mutation; default is 0.3. Does not need to be specified. |
Pi.N |
probability of taking -1 in mutation; default is 0.3 Does not need to be specified. |
NI |
number if islands; default is 7. Does not need to be specified. |
taper |
half width of taper used in spectral estimate; .5 (default) is full taper Does not need to be specified. |
min.freq, max.freq |
the frequency range (min.freq, max.freq) over which to calculate the Whittle likelihood; the default is (0, .5). Does not need to be specified. If min > max, the roles are reversed, and reset to the default if either is out of range. |
Details
Details my be found in Stoffer, D. S. (2023). AutoSpec: Detection of narrowband frequency changes in time series. Statistics and Its Interface, 16(1), 97-108. doi:10.4310/21-SII703
Value
Returns three values, (1) the breakpoints including the endpoints, (2) the number of segments, and (3) the segment kernel orders. See the examples.
Note
The GA is a stochastic optimization procedure and consequently will give different results at each run. It is a good idea to run the algorithm a few times before coming to a final decision.
Author(s)
D.S. Stoffer
Source
The genetic algorithm code is adapted from R code provided to us by Rex Cheung (https://www.linkedin.com/in/rexcheung). The code originally supported Aue, Cheung, Lee, & Zhong (2014). Segmented model selection in quantile regression using the minimum description length principle. JASA, 109, 1241-1256. A similar version also supported Davis, Lee, & Rodriguez-Yam (2006). Structural break estimation for nonstationary time series models. JASA, 101, 223-239.
References
You can find demonstrations of astsa capabilities at FUN WITH ASTSA.
The most recent version of the package can be found at https://github.com/nickpoison/astsa/.
In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.
The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.
See Also
Examples
## Not run:
##-- simulation
set.seed(1)
num = 500
t = 1:num
w = 2*pi/25
d = 2*pi/150
x1 = 2*cos(w*t)*cos(d*t) + rnorm(num)
x2 = cos(w*t) + rnorm(num)
x = c(x1,x2)
##-- plot and periodogram (all action below 0.1)
tsplot(x, main='not easy to see the change')
mvspec(x)
##-- run procedure
autoSpec(x, max.freq=.1)
##-- output (yours will be slightly different -
##-- the nature of GA)
# returned breakpoints include the endpoints
# $breakpoints
# [1] 1 503 1000
#
# $number_of_segments
# [1] 2
#
# $segment_kernel_orders_m
# [1] 2 4
##-- plot everything
par(mfrow=c(3,1))
tsplot(x, col=4)
abline(v=503, col=6, lty=2, lwd=2)
mvspec(x[1:502], kernel=bart(2), taper=.5, main='segment 1', col=4, xlim=c(0,.25))
mvspec(x[503:1000], kernel=bart(4), taper=.5, main='segment 2', col=4, xlim=c(0,.25))
## End(Not run)