AFF_scaled_stream_jumpdetect: Change detection using the AFF method

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

Change detection using the AFF method

Description

Original implementation in R of the AFF

Usage

AFF_scaled_stream_jumpdetect(stream, BL, affparams)

Arguments

stream	The stream of observations.
BL	The burn-in length, used to estimate the mean and variance.
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.

Value

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

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