autoParm: autoParm - Structural Break Estimation Using AR Models

In astsa: Applied Statistical Time Series Analysis

autoParm - Structural Break Estimation Using AR Models

Description

Uses minimum description length (MDL) to fit piecewise AR processes with the goal of detecting changepoints in time series. Optimization is accomplished via a genetic algorithm (GA).

Usage

autoParm(xdata, Pi.B = NULL, Pi.C = NULL, PopSize = 70, generation = 70, P0 = 20, 
         Pi.P = 0.3, Pi.N = 0.3, NI = 7)

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.
P0	maximum AR order; default is 20. If larger than 20, it 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.

Details

Details my be found in Davis, Lee, & Rodriguez-Yam (2006). Structural break estimation for nonstationary time series models. JASA, 101, 223-239. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/016214505000000745")}

Value

Returns three values, (1) the breakpoints including the endpoints, (2) the number of segments, and (3) the segment AR 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 code is adapted from R code provided to us by Rex Cheung (https://www.linkedin.com/in/rexcheung).

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

autoSpec

Examples

## Not run: 

##-- simulation
x1 = sarima.sim(ar=c(1.69, -.81), n=500)
x2 = sarima.sim(ar=c(1.32, -.81), n=500) 
x = c(x1, x2)

##-- look at the data
tsplot(x)

##-- run procedure
autoParm(x)

##-- output (yours will be slightly different - 
##--         the nature of GA) 
# returned breakpoints include the endpoints 
# $breakpoints
# [1]    1  514 1000
# 
# $number_of_segments
# [1] 2
# 
# $segment_AR_orders
# [1] 2 2

## End(Not run)

astsa documentation built on May 29, 2024, 10:29 a.m.