Anomaly detector using histograms — hanr

Flags observations that fall into low-density histogram bins or outside the observed bin range.

Usage

hanr_histogram(density_threshold = 0.05)

Arguments

density_threshold: Numeric between 0 and 1. Minimum bin density to avoid being considered an anomaly (default 0.05).

Value

hanr_histogram 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 histogram-based detector
model <- hanr_histogram()

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

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

# Show detected anomalies
print(detection[(detection$event),])
#>    idx event    type
#> 1    1  TRUE anomaly
#> 50  50  TRUE anomaly