# AFF_scaled_stream_jumpdetect_prechange: Change detection using the AFF method, using prechange mean... In ffstream: Forgetting Factor Methods for Change Detection in Streaming Data

## Description

Original implementation in R of the AFF, with prechange parameters

## Usage

 1 AFF_scaled_stream_jumpdetect_prechange(stream, BL, affparams, mu0, sigma0)

## Arguments

 stream The stream of observations. BL The burn-in length - this won't actually be used, but is kept for historical reasons. affparams An unnamed list of parameters for the FFF algorithm. Consists of: lambdaThe value of the fixed forgetting factor (FFF). Should be in the range [0,1]. pThe value of the significance threshold, which was later renamed alpha (in the paper, not in this function). resettozeroA flag; if it zero, then the ffmean will be reset to zero after each change. Usually set to 1 (i.e. do not reset). u_initThe initial value of u. Should be set to 0. v_initThe initial value of v. Should be set to 0. w_initThe initial value of w. Should be set to 0. affmean_initThe initial value of the forgetting factor mean, ffmean. Should be set to 0. affvar_initThe initial value of the forgetting factor variance, ffvar. Should be set to 0. low_boundThe lower bound for lambda. Usually set to 0.6. up_boundThe upper bound for lambda. Usually set to 1. signchosenThe sign used in the gradient. descent. Usually set to -1. alphaThe value of the step size in the gradient descent step. In the paper it is referred to as ε. Usually 0.01, or otherwise 0.1 or 0.001. mu0 The prechange mean, which is assumed known in this context sigma0 The prechange standard deviation, which is assumed known in this context

## Value

A vector with the values of the adaptive forgetting factor \overrightarrow{λ}.

ffstream documentation built on May 29, 2017, 10:33 p.m.