hanct_kmeans: Anomaly detector using kmeans

In harbinger: A Unified Time Series Event Detection Framework

Anomaly detector using kmeans

Description

Anomaly detection using kmeans The kmeans is applied to the time series. When seq equals one, observations distant from the closest centroids are labeled as anomalies. When seq is grater than one, sequences distant from the closest centroids are labeled as discords. It wraps the kmeans presented in the stats library.

Usage

hanct_kmeans(seq = 1, centers = NA)

Arguments

seq	sequence size
centers	number of centroids

Value

hanct_kmeans object

References

• 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 anomaly example data
data(examples_anomalies)

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

# Configure k-means detector
model <- hanct_kmeans()

# Fit the model
model <- fit(model, dataset$serie)

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

# Show detected events
print(detection[(detection$event),])

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