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

Change detection using the AFF method, using prechange mean and vairance

Description

Original implementation in R of the AFF, with prechange parameters

Usage

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 \epsilon. 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{\lambda}.

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