Hard evaluation of event detection producing confusion matrix and common metrics (accuracy, precision, recall, F1, etc.).
References
Harbinger documentation: https://cefet-rj-dal.github.io/harbinger
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)
dataset <- examples_anomalies$simple
head(dataset)
#> serie event
#> 1 1.0000000 FALSE
#> 2 0.9689124 FALSE
#> 3 0.8775826 FALSE
#> 4 0.7316889 FALSE
#> 5 0.5403023 FALSE
#> 6 0.3153224 FALSE
# Configure a change-point detector (GARCH)
model <- hcp_garch()
# Fit the detector
model <- fit(model, dataset$serie)
# Run detection
detection <- detect(model, dataset$serie)
# Show detected events
print(detection[(detection$event),])
#> idx event type
#> 52 52 TRUE changepoint
# Evaluate detections
evaluation <- evaluate(har_eval(), detection$event, dataset$event)
print(evaluation$confMatrix)
#> event
#> detection TRUE FALSE
#> TRUE 0 1
#> FALSE 1 99
# Plot the results
grf <- har_plot(model, dataset$serie, detection, dataset$event)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> ℹ The deprecated feature was likely used in the harbinger package.
#> Please report the issue at
#> <https://github.com/cefet-rj-dal/harbinger/issues>.
plot(grf)
