Hard evaluation of event detection producing confusion matrix and common metrics (accuracy, precision, recall, F1, etc.).
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$simple
head(dataset)
#> serie event
#> 1 1.0000000 FALSE
#> 2 0.9689124 FALSE
#> 3 0.8775826 FALSE
#> 4 0.7316889 FALSE
#> 5 0.5403023 FALSE
#> 6 0.3153224 FALSE
# Configure a change-point detector (GARCH)
model <- hcp_garch()
# Fit the detector
model <- fit(model, dataset$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)
