% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/har_ensemble_fuzzy.R
\name{har_ensemble_fuzzy}
\alias{har_ensemble_fuzzy}
\title{Harbinger Fuzzy Ensemble}
\usage{
har_ensemble_fuzzy(...)
}
\arguments{
\item{...}{One or more detector objects.}
}
\value{
A \code{har_ensemble_fuzzy} object.
}
\description{
Score-based ensemble across multiple Harbinger detectors with optional
temporal fuzzification, thresholding, and non-maximum suppression.

This variant preserves the richer aggregation logic that was previously mixed
into \code{har_ensemble()}, but keeps the simple majority-vote ensemble separate.
}
\examples{
library(daltoolbox)

data(examples_changepoints)
dataset <- examples_changepoints$simple

model <- har_ensemble_fuzzy(
  hcp_scp(sw = 30),
  hcp_chow(),
  hcp_cf_arima(sw_size = 10)
)
model <- fit(model, dataset$serie)
detection <- detect(model, dataset$serie, time_tolerance = 8, use_nms = TRUE)
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
}
}
