Change-point detection is related to event/trend change detection. Change Finder ETS detects change points based on deviations relative to trend component (T), a seasonal component (S), and an error term (E) model doi:10.1109/TKDE.2006.1599387. It wraps the ETS model presented in the forecast library.
Examples
library(daltoolbox)
#loading the example database
data(examples_changepoints)
#Using simple example
dataset <- examples_changepoints$simple
head(dataset)
#> serie event
#> 1 0.00 FALSE
#> 2 0.25 FALSE
#> 3 0.50 FALSE
#> 4 0.75 FALSE
#> 5 1.00 FALSE
#> 6 1.25 FALSE
# setting up change point method
model <- hcp_cf_ets()
# fitting the model
model <- fit(model, dataset$serie)
detection <- detect(model, dataset$serie)
# filtering detected events
print(detection[(detection$event),])
#> idx event type
#> 35 35 TRUE anomaly
#> 51 51 TRUE changepoint
#> 58 58 TRUE anomaly
#> 87 87 TRUE changepoint