Hybrid Approach to Sparse Group Fused Lasso
Fast hybrid algorithm for joint model segmentation and sparse estimation in high-dimensional piecewise linear regression models.
The algorithm finds global solutions to the sparse group fused lasso (SGFL) problem whose objective function is the sum of a squared loss function (to control fit to the data), an elastic net penalty (to promote sparsity in regression coefficients), and a total variation penalty (to promote parsimony in detected change points or segments).
install.packages(c("foreach", "glmnet", "Matrix", "Rcpp", "RcppArmadillo")) # package dependencies and imports
install.packages("devtools")
library(devtools)
install_github("ddegras/sparseGFL")
SGFL: solve SGFL for piecewise regression model y(t) = X(t) b(t) + e(t) (matrix predictors X, vector regression coefficients b)SGFL.AXY: solve SGFL for piecewise regression model y(t) = A(t) x(t) + e(t) (vector predictors x, matrix regression coefficients A)SGFL.VAR: solve SGFL for piecewise vector autoregressive (VAR) model y(t) = A1(t) y(t-1) + ... + Ap(t) y(t-p) + e(t)
Degras, D. "Sparse group fused lasso for model segmentation: a hybrid approach." Advances in Data Analysis and Classification 15, 625–671 (2021).