% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hanr_rtad.R
\name{hanr_rtad}
\alias{hanr_rtad}
\title{Resilient Transformation Anomaly Detector (RTAD)}
\usage{
hanr_rtad(sw_size = 30, noise = 0.001, trials = 5, sigma = sd)
}
\arguments{
\item{sw_size}{Sliding window size used to compute local dispersion.}

\item{noise}{CEEMD noise amplitude.}

\item{trials}{Number of CEEMD trials.}

\item{sigma}{Function used to compute local dispersion.}
}
\value{
\code{hanr_rtad} object
}
\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.
}
\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),])

}
\references{
\itemize{
\item 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
}
}
