hanr_rtad: Resilient Transformation Anomaly Detector (RTAD)

In harbinger: A Unified Time Series Event Detection Framework

Resilient Transformation Anomaly Detector (RTAD)

Description

Hybrid anomaly detector built from the Resilient Transformation (RT) proposed in the RT/RTAD paper. The series is decomposed with CEEMD, the highest-frequency structure is selected from IMF roughness, the transformed signal is differentiated, and local dispersion is used to normalize deviations before thresholding.

RTAD is not a generic wrapper around EMD. It is the standalone detector obtained when the resilient transformation is coupled with a simple decision rule.

Usage

hanr_rtad(sw_size = 30, noise = 0.001, trials = 5, sigma = sd)

Arguments

sw_size	Sliding window size used to compute local dispersion.
noise	CEEMD noise amplitude.
trials	Number of CEEMD trials.
sigma	Function used to compute local dispersion.

Value

hanr_rtad 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)
library(zoo)

# Load anomaly example data
data(examples_anomalies)

# Use a simple example
dataset <- examples_anomalies$simple
head(dataset)

# Configure RTAD detector
model <- hanr_rtad()

# Fit the model
model <- fit(model, dataset$serie)

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

# Show detected events
print(detection[(detection$event),])

harbinger documentation built on May 14, 2026, 5:06 p.m.