update_mean

Function to sequentially update the FF mean

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

update_mean function

<dl><dt>ffmean_old</dt>
<dd>The old (previous) value of <code>ffmean</code>.</dd>
<dt>w_new</dt>
<dd>The new (current) value of <code>w</code>.</dd>
<dt>x_new</dt>
<dd>The current observation.</dd></dl>

Arguments

Update the FF mean — update_mean

<dl>

<dt>ffmean_old</dt>
<dd>The old (previous) value of <code>ffmean</code>.</dd>


<dt>w_new</dt>
<dd>The new (current) value of <code>w</code>.</dd>


<dt>x_new</dt>
<dd>The current observation.</dd>

</dl>

update_mean: Update the FF mean

Description

Usage

Arguments