cpp_detectAFFMeanMultiple

Given a vector <code>x</code>, a threshold <code>alpha</code>, a step size <code>eta</code>, 
and a burn-in length <code>BL</code>, returns a list containing the changepoints.
Algorithm is implemented in 'C++'.

internal

An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in 'C++' and uses 'Rcpp'. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic cumulative sum ('CUSUM') and exponentially weighted moving average ('EWMA') methods, are included.

Dean Bodenham

ffstream

Forgetting Factor Methods for Change Detection in Streaming Data

cpp_detectAFFMeanMultiple function

<dl><dt>x</dt>
<dd>Vector of numeric values values.</dd>
<dt>alpha</dt>
<dd>Value for the significance threshold in \((0,1)\).</dd>
<dt>eta</dt>
<dd>Value for the step size in \((0,1)\).</dd>
<dt>BL</dt>
<dd>Value for the burn-in length.</dd></dl>

Arguments

Search for multiple changepoints in the mean using AFF — cpp_detectAFFMeanMultiple

<dl>

<dt>x</dt>
<dd>Vector of numeric values values.</dd>


<dt>alpha</dt>
<dd>Value for the significance threshold in \((0,1)\).</dd>


<dt>eta</dt>
<dd>Value for the step size in \((0,1)\).</dd>


<dt>BL</dt>
<dd>Value for the burn-in length.</dd>

</dl>

cpp_detectAFFMeanMultiple: Search for multiple changepoints in the mean using AFF

Description

Usage

Value

Arguments