hanr_rtad: Anomaly and change point detector using RTAD

In harbinger: A Unified Time Series Event Detection Framework

Anomaly and change point detector using RTAD

Description

Anomaly and change point detection using RTAD The RTAD model adjusts to the time series. Observations distant from the model are labeled as anomalies. It wraps the EMD model presented in the hht library.

Usage

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

Arguments

sw_size	sliding window size (default 30)
noise	noise
trials	trials
sigma	function to compute the 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 Feb. 11, 2026, 9:06 a.m.