% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hanr_fft_binseg.R
\name{hanr_fft_binseg}
\alias{hanr_fft_binseg}
\title{Anomaly Detector using FFT with Binary Segmentation Cutoff}
\usage{
hanr_fft_binseg()
}
\value{
\code{hanr_fft_binseg} object
}
\description{
This detector combines FFT-based spectral filtering with a Binary Segmentation
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 is part of the HARBINGER framework and returns an object of class \code{hanr_fft_binseg}.
}
\examples{
library(daltoolbox)

# Load anomaly example data
data(examples_anomalies)

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

# Configure FFT+BinSeg detector
model <- hanr_fft_binseg()

# 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
}
}
