update_w

Function to sequentially update the weight used in the computation of
the forgetting factor 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_w function

<dl><dt>w_old</dt>
<dd>The old (previous) value of <code>w</code>.</dd>
<dt>lambda_old</dt>
<dd>The value of the forgetting factor. No real need to
add 'old' here, in the adaptive scheme, these are the
the values from the previous iteration.</dd></dl>

Arguments

Update the weight — update_w

<dl>

<dt>w_old</dt>
<dd>The old (previous) value of <code>w</code>.</dd>


<dt>lambda_old</dt>
<dd>The value of the forgetting factor. No real need to
add 'old' here, in the adaptive scheme, these are the
the values from the previous iteration.</dd>

</dl>

update_w: Update the weight

Description

Usage

Arguments