% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hcp_kswin.R
\name{hcp_kswin}
\alias{hcp_kswin}
\title{KSWIN change-point detector}
\usage{
hcp_kswin(window_size = 100, stat_size = 30, alpha = 0.005, data = NULL)
}
\arguments{
\item{window_size}{Size of the sliding window.}

\item{stat_size}{Size of the statistic subwindow used for the KS test.}

\item{alpha}{Significance level for the KS test.}

\item{data}{Optional initial window content.}
}
\value{
An \code{hcp_kswin} object.
}
\description{
Kolmogorov-Smirnov Windowing for univariate time series. The detector keeps a
sliding window, compares an early sample against the most recent observations,
and flags a changepoint when the two empirical distributions differ
significantly.

This implementation is restricted to univariate numeric series and is intended
to capture virtual drift on the signal directly, without any classifier.
}
\references{
\itemize{
\item Raab C, Heusinger M, Schleif FM (2020). Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing.
\item Bifet A, Gavaldà R (2007). Learning from time-changing data with adaptive windowing. SIAM International Conference on Data Mining.
}
}
