Window-based change-point detection that compares two local linear models fitted on each sliding window: one using the full window and another using the same window split around its central observation. The difference between the two residual summaries is used as the change score.
The method is called "seminal" because the paper defines a seminal point for each window family, i.e. the central observation used to split the local regression into two sides. This makes the detector a local family method based on windows rather than a global segmentation algorithm.
References
The seminal change-point paper referenced in
Event Detection from Time Series Data.Ogasawara, E., Salles, R., Porto, F., Pacitti, E. Event Detection in Time Series. 1st ed. Cham: Springer Nature Switzerland, 2025. doi:10.1007/978-3-031-75941-3
Examples
library(daltoolbox)
# Load change-point example data
data(examples_changepoints)
# Use a simple example
dataset <- examples_changepoints$simple
head(dataset)
#> serie event
#> 1 0.00 FALSE
#> 2 0.25 FALSE
#> 3 0.50 FALSE
#> 4 0.75 FALSE
#> 5 1.00 FALSE
#> 6 1.25 FALSE
# Configure the seminal change-point detector
model <- hcp_scp()
# Fit the model
model <- fit(model, dataset$serie)
# Run detection
detection <- detect(model, dataset$serie)
# Show detected change points
print(detection[(detection$event),])
#> idx event type
#> 50 50 TRUE changepoint