% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hcp_page_hinkley.R
\name{hcp_page_hinkley}
\alias{hcp_page_hinkley}
\title{Page-Hinkley change-point detector}
\usage{
hcp_page_hinkley(
  min_instances = 30,
  delta = 0.005,
  threshold = 50,
  alpha = 1 - 1e-04
)
}
\arguments{
\item{min_instances}{Minimum number of observations required before a change
can be reported.}

\item{delta}{Slack term subtracted from the deviation score.}

\item{threshold}{Detection threshold for the cumulative statistic.}

\item{alpha}{Forgetting factor applied to the cumulative score.}
}
\value{
An \code{hcp_page_hinkley} object.
}
\description{
Online change-point detection for univariate time series using the classical
Page-Hinkley statistic. The detector accumulates deviations from the running
mean and raises a changepoint when the cumulative score crosses the
configured threshold.

This implementation is restricted to univariate numeric series. It is meant
to capture virtual drift on the observed signal directly, without any
classifier or multivariate preprocessing.
}
\references{
\itemize{
\item Page ES (1954). Continuous Inspection Schemes. Biometrika, 41(1/2), 100-115.
\item Raab C, Heusinger M, Schleif FM (2020). Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing.
}
}
