% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hanr_remd.R
\name{hanr_remd}
\alias{hanr_remd}
\title{Anomaly detector using REMD}
\usage{
hanr_remd(noise = 0.1, trials = 5)
}
\arguments{
\item{noise}{Noise amplitude for the decomposition.}

\item{trials}{Number of trials used by the decomposition step.}
}
\value{
\code{hanr_remd} object
}
\description{
Anomaly detection using REMD with EMD-based decomposition.
The detector decomposes the series, selects components according to curvature,
and flags large residual deviations as anomalies.
Wraps the EMD-based model presented in the \code{forecast} package.
}
\examples{
library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

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

# Configure REMD detector
model <- hanr_remd()

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

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

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

}
\references{
\itemize{
\item Souza, J., Paixão, E., Fraga, F., Baroni, L., Alves, R. F. S., Belloze, K., Dos Santos, J.,
Bezerra, E., Porto, F., Ogasawara, E. REMD: A Novel Hybrid Anomaly Detection Method Based on
EMD and ARIMA. Proceedings of the International Joint Conference on Neural Networks, 2024.
doi:10.1109/IJCNN60899.2024.10651192
}
}
