Anomaly detector using k-means — hanct

Distance-based anomaly and discord detection using k-means clustering. The detector clusters the series and flags observations or subsequences that are far from the nearest centroid. When seq equals one, isolated observations are labeled as anomalies. When seq is greater than one, subsequences are labeled as discords. Wraps the kmeans implementation from the stats package.

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)
#>       serie event
#> 1 1.0000000 FALSE
#> 2 0.9689124 FALSE
#> 3 0.8775826 FALSE
#> 4 0.7316889 FALSE
#> 5 0.5403023 FALSE
#> 6 0.3153224 FALSE

# 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),])
#>    idx event    type
#> 50  50  TRUE anomaly