computeFFFMean

Given a vector <code>x</code> and a value <code>lambda</code> for a fixed forgetting
factor, returns the value of the fixed forgetting factor mean
\(\bar{x}_{N, \lambda}\), where \(N\) is the length of <code>x</code>.
Algorithm is implemented in 'C++'.

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

computeFFFMean function

<dl><dt>x</dt>
<dd>Vector of numeric values values. Default is <code>c(0)</code>, 
a vector of one element (zero)</dd>
<dt>lambda</dt>
<dd>Value for the fixed forgetting factor in \([0,1]\).
Default is <code>lambda=0.99</code>.</dd></dl>

Arguments

Author

Quick computation of FFF mean of a given vector — computeFFFMean

<dl>

<dt>x</dt>
<dd>Vector of numeric values values. Default is <code>c(0)</code>, 
a vector of one element (zero)</dd>


<dt>lambda</dt>
<dd>Value for the fixed forgetting factor in \([0,1]\).
Default is <code>lambda=0.99</code>.</dd>

</dl>

computeFFFMean: Quick computation of FFF mean of a given vector

Description

Usage

Value

Arguments

Author

References

See Also