Segment a time series using a variety of algorithms

A wrapper function that encapsulates various algorithms for detecting changepoint sets in univariate time series.

Usage

segment(x, method = "null", ...)

# S3 method for class 'tbl_ts'
segment(x, method = "null", ...)

# S3 method for class 'xts'
segment(x, method = "null", ...)

# S3 method for class 'numeric'
segment(x, method = "null", ...)

# S3 method for class 'ts'
segment(x, method = "null", ...)

Arguments

x: a numeric vector coercible into a stats::ts object
method: a character string indicating the algorithm to use. See Details.
...: arguments passed to methods

Value

An object of class tidycpt.

Details

Currently, segment() can use the following algorithms, depending on the value of the method argument:

pelt: Uses the PELT algorithm as implemented in segment_pelt(), which wraps either changepoint::cpt.mean() or changepoint::cpt.meanvar(). The segmenter is of class cpt.
binseg: Uses the Binary Segmentation algorithm as implemented by changepoint::cpt.meanvar(). The segmenter is of class cpt.
segneigh: Uses the Segmented Neighborhood algorithm as implemented by changepoint::cpt.meanvar(). The segmenter is of class cpt.
single-best: Uses the AMOC criteria as implemented by changepoint::cpt.meanvar(). The segmenter is of class cpt.
wbs: Uses the Wild Binary Segmentation algorithm as implemented by wbs::wbs(). The segmenter is of class wbs.
ga: Uses the Ggnetic algorithm implemented by segment_ga(), which wraps GA::ga(). The segmenter is of class tidyga.
ga-shi: Uses the genetic algorithm implemented by segment_ga_shi(), which wraps segment_ga(). The segmenter is of class tidyga.
ga-coen: Uses Coen's heuristic as implemented by segment_ga_coen(). The segmenter is of class tidyga. This implementation supersedes the following one.
coen: Uses Coen's heuristic as implemented by segment_coen(). The segmenter is of class seg_basket(). Note that this function is deprecated.
random: Uses a random basket of changepoints as implemented by segment_ga_random(). The segmenter is of class tidyga.
manual: Uses the vector of changepoints in the tau argument. The segmenter is of class seg_cpt`.
null: The default. Uses no changepoints. The segmenter is of class seg_cpt`.

Examples

# Segment a time series using PELT
segment(DataCPSim, method = "pelt")
#> A tidycpt object
#> Class 'cpt' : Changepoint Object
#>        ~~   : S4 class containing 12 slots with names
#>               cpttype date version data.set method test.stat pen.type pen.value minseglen cpts ncpts.max param.est 
#> 
#> Created on  : Wed Apr 24 21:56:29 2024 
#> 
#> summary(.)  :
#> ----------
#> Created Using changepoint version 2.2.4 
#> Changepoint type      : Change in mean and variance 
#> Method of analysis    : PELT 
#> Test Statistic  : Normal 
#> Type of penalty       : MBIC with value, 27.99769 
#> Minimum Segment Length : 2 
#> Maximum no. of cpts   : Inf 
#> Changepoint Locations : 547 822 972 
#> List of 6
#>  $ data         : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ tau          : int [1:3] 547 822 972
#>  $ region_params: tibble [4 × 3] (S3: tbl_df/tbl/data.frame)
#>   ..$ region           : chr [1:4] "[0,547)" "[547,822)" "[822,972)" "[972,1.1e+03]"
#>   ..$ param_mu         : num [1:4] 35.3 58.1 96.7 155.9
#>   ..$ param_sigma_hatsq: Named num [1:4] 127 372 924 2442
#>   .. ..- attr(*, "names")= chr [1:4] "[0,547)" "[547,822)" "[822,972)" "[972,1.1e+03]"
#>  $ model_params : NULL
#>  $ fitted_values: num [1:1096] 35.3 35.3 35.3 35.3 35.3 ...
#>  $ model_name   : chr "meanvar"
#>  - attr(*, "class")= chr "mod_cpt"

# Segment a time series using PELT and the BIC penalty
segment(DataCPSim, method = "pelt", penalty = "BIC")
#> A tidycpt object
#> Class 'cpt' : Changepoint Object
#>        ~~   : S4 class containing 12 slots with names
#>               cpttype date version data.set method test.stat pen.type pen.value minseglen cpts ncpts.max param.est 
#> 
#> Created on  : Wed Apr 24 21:56:29 2024 
#> 
#> summary(.)  :
#> ----------
#> Created Using changepoint version 2.2.4 
#> Changepoint type      : Change in mean and variance 
#> Method of analysis    : PELT 
#> Test Statistic  : Normal 
#> Type of penalty       : BIC with value, 20.99827 
#> Minimum Segment Length : 2 
#> Maximum no. of cpts   : Inf 
#> Changepoint Locations : 547 822 972 
#> List of 6
#>  $ data         : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ tau          : int [1:3] 547 822 972
#>  $ region_params: tibble [4 × 3] (S3: tbl_df/tbl/data.frame)
#>   ..$ region           : chr [1:4] "[0,547)" "[547,822)" "[822,972)" "[972,1.1e+03]"
#>   ..$ param_mu         : num [1:4] 35.3 58.1 96.7 155.9
#>   ..$ param_sigma_hatsq: Named num [1:4] 127 372 924 2442
#>   .. ..- attr(*, "names")= chr [1:4] "[0,547)" "[547,822)" "[822,972)" "[972,1.1e+03]"
#>  $ model_params : NULL
#>  $ fitted_values: num [1:1096] 35.3 35.3 35.3 35.3 35.3 ...
#>  $ model_name   : chr "meanvar"
#>  - attr(*, "class")= chr "mod_cpt"

# Segment a time series using Binary Segmentation
segment(DataCPSim, method = "binseg", penalty = "BIC")
#> A tidycpt object
#> Class 'cpt' : Changepoint Object
#>        ~~   : S4 class containing 14 slots with names
#>               cpts.full pen.value.full data.set cpttype method test.stat pen.type pen.value minseglen cpts ncpts.max param.est date version 
#> 
#> Created on  : Wed Apr 24 21:56:29 2024 
#> 
#> summary(.)  :
#> ----------
#> Created Using changepoint version 2.2.4 
#> Changepoint type      : Change in mean and variance 
#> Method of analysis    : BinSeg 
#> Test Statistic  : Normal 
#> Type of penalty       : BIC with value, 20.99827 
#> Minimum Segment Length : 2 
#> Maximum no. of cpts   : 5 
#> Changepoint Locations : 547 809 972 
#> Range of segmentations:
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]  809   NA   NA   NA   NA
#> [2,]  809  547   NA   NA   NA
#> [3,]  809  547  972   NA   NA
#> [4,]  809  547  972  822   NA
#> [5,]  809  547  972  822  813
#> 
#>  For penalty values: 1485.679 462.0479 160.3649 15.04514 15.04514 
#> List of 6
#>  $ data         : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ tau          : int [1:3] 547 809 972
#>  $ region_params: tibble [4 × 3] (S3: tbl_df/tbl/data.frame)
#>   ..$ region           : chr [1:4] "[0,547)" "[547,809)" "[809,972)" "[972,1.1e+03]"
#>   ..$ param_mu         : num [1:4] 35.3 57.9 94 155.9
#>   ..$ param_sigma_hatsq: Named num [1:4] 127 341 1015 2442
#>   .. ..- attr(*, "names")= chr [1:4] "[0,547)" "[547,809)" "[809,972)" "[972,1.1e+03]"
#>  $ model_params : NULL
#>  $ fitted_values: num [1:1096] 35.3 35.3 35.3 35.3 35.3 ...
#>  $ model_name   : chr "meanvar"
#>  - attr(*, "class")= chr "mod_cpt"

# Segment a time series using a random changepoint set
segment(DataCPSim, method = "random")
#> Seeding initial population with probability: 0.0063863343681642
#> A tidycpt object
#> An object of class "ga"
#> 
#> Call:
#> GA::ga(type = "binary", fitness = obj_fun, nBits = n, population = ..1,     maxiter = 1)
#> 
#> Available slots:
#>  [1] "data"          "model_fn_args" "call"          "type"         
#>  [5] "lower"         "upper"         "nBits"         "names"        
#>  [9] "popSize"       "iter"          "run"           "maxiter"      
#> [13] "suggestions"   "population"    "elitism"       "pcrossover"   
#> [17] "pmutation"     "optim"         "fitness"       "summary"      
#> [21] "bestSol"       "fitnessValue"  "solution"     
#> List of 6
#>  $ data         : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ tau          : int [1:7] 34 107 369 553 840 875 933
#>  $ region_params: tibble [8 × 2] (S3: tbl_df/tbl/data.frame)
#>   ..$ region  : chr [1:8] "[0,34)" "[34,107)" "[107,369)" "[369,553)" ...
#>   ..$ param_mu: num [1:8] 37.2 37.1 35.1 35.1 60.3 ...
#>  $ model_params : Named num 640
#>   ..- attr(*, "names")= chr "sigma_hatsq"
#>  $ fitted_values: num [1:1096] 37.2 37.2 37.2 37.2 37.2 ...
#>  $ model_name   : chr "meanshift_norm"
#>  - attr(*, "class")= chr "mod_cpt"

# Segment a time series using a manually-specified changepoint set
segment(DataCPSim, method = "manual", tau = c(826))
#> A tidycpt object
#> List of 8
#>  $ data        : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ pkg         : chr "tidychangepoint"
#>  $ algorithm   : chr "manual"
#>  $ changepoints: num 826
#>  $ fitness     : Named num 10571
#>   ..- attr(*, "names")= chr "BIC"
#>  $ seg_params  : list()
#>  $ model_name  : chr "meanshift_norm"
#>  $ penalty     : chr "BIC"
#>  - attr(*, "class")= chr "seg_cpt"
#> List of 6
#>  $ data         : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ tau          : int 826
#>  $ region_params: tibble [2 × 2] (S3: tbl_df/tbl/data.frame)
#>   ..$ region  : chr [1:2] "[0,826)" "[826,1.1e+03]"
#>   ..$ param_mu: num [1:2] 43.2 123.8
#>  $ model_params : Named num 882
#>   ..- attr(*, "names")= chr "sigma_hatsq"
#>  $ fitted_values: num [1:1096] 43.2 43.2 43.2 43.2 43.2 ...
#>  $ model_name   : chr "meanshift_norm"
#>  - attr(*, "class")= chr "mod_cpt"

# Segment a time series using a null changepoint set
segment(DataCPSim)
#> A tidycpt object
#> List of 8
#>  $ data        : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ pkg         : chr "tidychangepoint"
#>  $ algorithm   : chr "manual"
#>  $ changepoints: NULL
#>  $ fitness     : Named num 11503
#>   ..- attr(*, "names")= chr "BIC"
#>  $ seg_params  : list()
#>  $ model_name  : chr "meanshift_norm"
#>  $ penalty     : chr "BIC"
#>  - attr(*, "class")= chr "seg_cpt"
#> List of 6
#>  $ data         : Time-Series [1:1096] from 1 to 1096: 35.5 29 35.6 33 29.5 ...
#>  $ tau          : int(0) 
#>  $ region_params: tibble [1 × 2] (S3: tbl_df/tbl/data.frame)
#>   ..$ region  : chr "[0,1.1e+03]"
#>   ..$ param_mu: num 63.2
#>  $ model_params : Named num 2089
#>   ..- attr(*, "names")= chr "sigma_hatsq"
#>  $ fitted_values: num [1:1096] 63.2 63.2 63.2 63.2 63.2 ...
#>  $ model_name   : chr "meanshift_norm"
#>  - attr(*, "class")= chr "mod_cpt"