initFFFMeanCD

This function makes it simple to initalise an FFF object.

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

initFFFMeanCD function

<dl><dt>alpha</dt>
<dd>The value of the significance level. 
Default value is <code>0.01</code>, although it is
recommended that the user set this parameter.</dd>
<dt>lambda</dt>
<dd>The value of the fixed forgetting factor. 
Default value is <code>lambda=1</code>.</dd>
<dt>BL</dt>
<dd>The length of the burn-in region. Default value is <code>BL=50</code>.</dd></dl>

Arguments

Initialisation of FFF mean change detector — initFFFMeanCD

<dl>

<dt>alpha</dt>
<dd>The value of the significance level. 
Default value is <code>0.01</code>, although it is
recommended that the user set this parameter.</dd>


<dt>lambda</dt>
<dd>The value of the fixed forgetting factor. 
Default value is <code>lambda=1</code>.</dd>


<dt>BL</dt>
<dd>The length of the burn-in region. Default value is <code>BL=50</code>.</dd>

</dl>

initFFFMeanCD: Initialisation of FFF mean change detector

Description

Usage

Value

Arguments

Examples