computeAFFMean

Given a vector <code>x</code> and a value <code>eta</code> for step-size
in the stochastic gradient descent for the adaptive forgetting
factor, this returns the value of the fixed forgetting factor mean
\(\bar{x}_{N, \overrightarrow{\lambda} }\), 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

computeAFFMean 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>eta</dt>
<dd>Value for the step size in the gradient descent step. 
Default is <code>eta=0.01</code>.</dd></dl>

Arguments

Author

Quick computation of AFF mean of a given vector — computeAFFMean

<dl>

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


<dt>eta</dt>
<dd>Value for the step size in the gradient descent step. 
Default is <code>eta=0.01</code>.</dd>

</dl>

computeAFFMean: Quick computation of AFF mean of a given vector

Description

Usage

Value

Arguments

Author

References

See Also