hcp_amoc: At Most One Change (AMOC)

In harbinger: A Unified Time Series Event Detection Framework

At Most One Change (AMOC)

Description

Change-point detection method focusing on identifying at most one change in mean and/or variance. This is a wrapper around the AMOC implementation from the changepoint package.

Usage

hcp_amoc()

Details

AMOC detects a single most significant change point under a cost function optimized for a univariate series. It is useful when at most one structural break is expected.

Value

hcp_amoc object.

References

• Hinkley DV (1970). Inference about the change-point in a sequence of random variables. Biometrika, 57(1):1–17. doi:10.1093/biomet/57.1.1

• Killick R, Fearnhead P, Eckley IA (2012). Optimal detection of changepoints with a linear computational cost. JASA, 107(500):1590–1598.

Examples

library(daltoolbox)

# Load change-point example data
data(examples_changepoints)

# Use a simple example
dataset <- examples_changepoints$simple
head(dataset)

# Configure the AMOC detector
model <- hcp_amoc()

# Fit the detector (no-op for AMOC)
model <- fit(model, dataset$serie)

# Run detection
detection <- detect(model, dataset$serie)

# Show detected change point(s)
print(detection[(detection$event),])

harbinger documentation built on Feb. 11, 2026, 9:06 a.m.