compute_dynseg: Block segmentation using dynamical programming

In Lucas-Prates/blockcpd: Change Point Detection for Multiple Aligned Independent Time Series

Block segmentation using dynamical programming

Description

Computes the exact solution of the regularized loss optimization problem, providing change point locations and the parameters of each blocks. Should be called within fit_blockcpd

Usage

compute_dynseg(
  suff_stats,
  family,
  lambda = 1,
  nrow,
  ncol,
  min_block_size = min_block_size,
  max_blocks = ncol - 1,
  pen_func = bic_loss
)

Arguments

suff_stats	Sufficient statistics to perform change point analysis
family	The name of the family used to fit the model
lambda	Penalization constant
nrow	Number of rows or samples
ncol	Number of columns or variables
min_block_size	Minimum block size allowed. Default is 0, and the value must be smaller or equal to ncol.
max_blocks	Threshold on the number of block segments to fit the model. Set low values for this parameters if having performance issues on large data sets.
pen_func	A penalization function defined i integer intervals The function signature should be pen(left_index, right_index, nrow, ncol), where the left_index:right_index is the integer interval, nrow the sample size and ncol the number of variables/columns.

Lucas-Prates/blockcpd documentation built on Aug. 19, 2022, 12:55 a.m.