har_eval: Evaluation of event detection

In harbinger: A Unified Time Series Event Detection Framework

Evaluation of event detection

Description

Hard evaluation of event detection producing confusion matrix and common metrics (accuracy, precision, recall, F1, etc.).

Usage

har_eval()

Value

har_eval object

References

• Salles, R., Lima, J., Reis, M., Coutinho, R., Pacitti, E., Masseglia, F., Akbarinia, R., Chen, C., Garibaldi, J., Porto, F., Ogasawara, E. SoftED: Metrics for soft evaluation of time series event detection. Computers and Industrial Engineering, 2024. doi:10.1016/j.cie.2024.110728

Examples

library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

dataset <- examples_anomalies$simple
head(dataset)

# 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),])

# Evaluate detections
evaluation <- evaluate(har_eval(), detection$event, dataset$event)
print(evaluation$confMatrix)

# Plot the results
grf <- har_plot(model, dataset$serie, detection, dataset$event)
plot(grf)

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