# 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: `lambda`The value of the fixed forgetting factor (FFF). Should be in the range [0,1]. `p`The value of the significance threshold, which was later renamed `alpha` (in the paper, not in this function). `resettozero`A 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_init`The initial value of `u`. Should be set to 0. `v_init`The initial value of `v`. Should be set to 0. `w_init`The initial value of `w`. Should be set to 0. `affmean_init`The initial value of the forgetting factor mean, `ffmean`. Should be set to 0. `affvar_init`The initial value of the forgetting factor variance, `ffvar`. Should be set to 0. `low_bound`The lower bound for `lambda`. Usually set to `0.6`. `up_bound`The upper bound for `lambda`. Usually set to `1`. `signchosen`The sign used in the gradient. descent. Usually set to `-1`. `alpha`The 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 14, 2018, 9:08 a.m.