computeAFFMean: Quick computation of AFF mean of a given vector

In ffstream: Forgetting Factor Methods for Change Detection in Streaming Data

Quick computation of AFF mean of a given vector

Description

Given a vector x and a value eta 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 x. Algorithm is implemented in 'C++'.

Usage

computeAFFMean(x = c(0), eta = 0.01)

Arguments

x	Vector of numeric values values. Default is c(0), a vector of one element (zero)
eta	Value for the step size in the gradient descent step. Default is eta=0.01.

Value

The adaptive forgetting factor mean (scalar).

Author

Dean Bodenham

References

D. A. Bodenham and N. M. Adams (2016) Continuous monitoring for changepoints in data streams using adaptive estimation. Statistics and Computing doi:10.1007/s11222-016-9684-8

See Also

computeFFFMean

ffstream documentation built on May 31, 2023, 7:53 p.m.