EWMA_stream_jumpdetect

Original implementation in R of 'EWMA' change detector, now with
documentation.

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

EWMA_stream_jumpdetect function

<dl><dt>stream</dt>
<dd>The stream of observations.</dd>
<dt>BL</dt>
<dd>The burn-in length, used to estimate the mean and variance.</dd>
<dt>params</dt>
<dd>A list of parameters for the 'EWMA' algorithm. Consists of<dl>
 <dt><code>r</code></dt>
<dd>A control parameter which controls
 the rate of downweighting.</dd></dl></dd>
 <dt><code>L</code></dt>
<dd>A control parameter which determines 
 the width of the control limits.</dd> 
</dl>

Arguments

'EWMA' change detection for a stream in R with — EWMA_stream_jumpdetect

<dl>

<dt>stream</dt>
<dd>The stream of observations.</dd>


<dt>BL</dt>
<dd>The burn-in length, used to estimate the mean and variance.</dd>


<dt>params</dt>
<dd>A list of parameters for the 'EWMA' algorithm. Consists of<dl>
 <dt><code>r</code></dt>
<dd>A control parameter which controls
 the rate of downweighting.</dd>


 <dt><code>L</code></dt>
<dd>A control parameter which determines 
 the width of the control limits.</dd>

 
</dl></dd>

</dl>

EWMA_stream_jumpdetect: 'EWMA' change detection for a stream in R with

Description

Usage

Value

Arguments