astsa (version 2.1)

autoSpec: 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)

Value

Returns three values, (1) the breakpoints including the endpoints, (2) the number of segments, and (3) the segment kernel orders. See the examples.

Arguments

xdata

time series (of length n at least 100) to be analyzed; the ts attributes are stripped prior to the analysis

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 2*m0 + 1; default is 10. If larger than 20, m0 is reset to 20. Does not need to be specified.

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.

Author

D.S. Stoffer

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. tools:::Rd_expr_doi("10.4310/21-SII703")

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

autoParm

Examples

if (FALSE) {

##-- 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))   
}