% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hanr_fft_amoc.R
\name{hanr_fft_amoc}
\alias{hanr_fft_amoc}
\title{Anomaly Detector using FFT with AMOC Cutoff}
\usage{
hanr_fft_amoc()
}
\value{
\code{hanr_fft_amoc} object
}
\description{
This detector combines FFT-based spectral filtering with an AMOC change-point
cutoff on the power spectrum. Frequencies below the selected cutoff are removed,
the signal is reconstructed from the remaining high-frequency content, and the
residual is scored for anomalies.

This function extends the HARBINGER framework and returns an object of class \code{hanr_fft_amoc}.
}
\examples{
library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

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

# Configure FFT+AMOC detector
model <- hanr_fft_amoc()

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

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

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

}
\references{
\itemize{
\item Sobrinho, E. P., Souza, J., Lima, J., Giusti, L., Bezerra, E., Coutinho, R., Baroni, L.,
Pacitti, E., Porto, F., Belloze, K., Ogasawara, E. Fine-Tuning Detection Criteria for
Enhancing Anomaly Detection in Time Series. In: Simpósio Brasileiro de Banco de Dados
(SBBD). SBC, 29 Sep. 2025. doi:10.5753/sbbd.2025.247063
}
}
