update_var

Function to sequentially update the forgetting factor variance

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_var function

<dl><dt>ffvar</dt>
<dd>The new (current) value of the forgetting factor variance.</dd>
<dt>ffmean_old</dt>
<dd>The old (previous) value of the forgetting factor mean.
In fact, the formula requires this value, rather than
the current value of the FF mean.</dd>
<dt>lambda</dt>
<dd>The value of the forgetting factor.</dd>
<dt>v_old</dt>
<dd>The old (previous) value of <code>v</code>.</dd>
<dt>v_new</dt>
<dd>The new (current) value of <code>v</code>.</dd>
<dt>w_new</dt>
<dd>The new (current) value of <code>w</code>.</dd>
<dt>x_new</dt>
<dd>The new (current) observation.</dd></dl>

Arguments

Update the FF variance — update_var

<dl>

<dt>ffvar</dt>
<dd>The new (current) value of the forgetting factor variance.</dd>


<dt>ffmean_old</dt>
<dd>The old (previous) value of the forgetting factor mean.
In fact, the formula requires this value, rather than
the current value of the FF mean.</dd>


<dt>lambda</dt>
<dd>The value of the forgetting factor.</dd>


<dt>v_old</dt>
<dd>The old (previous) value of <code>v</code>.</dd>


<dt>v_new</dt>
<dd>The new (current) value of <code>v</code>.</dd>


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


<dt>x_new</dt>
<dd>The new (current) observation.</dd>

</dl>

update_var: Update the FF variance

Description

Usage

Arguments