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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.

Usage

hcp_cf_ets(sw_size = 7)

Arguments

sw_size

Sliding window size

Value

hcp_cf_ets object

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