SOURCE-LINKED INTELLIGENCE

On Detecting Multiple Simultaneous Change-points in High Dimensional Non-Stationary Time Series

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

This paper studies the detection of multiple simultaneous (systematic) change points for high-dimensional nonstantionary economic and financial time series data. The analytic framework used is based on the standard and adaptive fused group lasso method, where the mixed L_{2,1} penalty is either uniform or re-weighted by data-dependent weights. This paper shows that, under appropriate conditions, this approach is L_2 consistent and, by adopting the data-dependent weights, could correctly select the change points with probability approaching unity (L_0 consis- tency). It quantifies the condition

Read original source ↗ Open in workspace

recordType

paper

region

Global