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.
Usage
hcp_page_hinkley(
min_instances = 30,
delta = 0.005,
threshold = 50,
alpha = 1 - 1e-04
)
Arguments
- min_instances
Minimum number of observations required before a change
can be reported.
- delta
Slack term subtracted from the deviation score.
- threshold
Detection threshold for the cumulative statistic.
- alpha
Forgetting factor applied to the cumulative score.
Value
An hcp_page_hinkley object.
References
Page ES (1954). Continuous Inspection Schemes. Biometrika, 41(1/2), 100-115.
Raab C, Heusinger M, Schleif FM (2020). Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing.