Kernel-Induced Self-Representation Learning for Concept Drift in Co-evolving Time Series
A representation-learning method for co-evolving time series that models each series as a nonlinear self-representation of the others in a reproducing-kernel Hilbert space, producing a coefficient matrix regularized toward a block-diagonal structure whose blocks are latent concepts. By recomputing this representation over sliding windows and tracking how series migrate between blocks, it identifies concepts, tracks concept drift, and forecasts emerging concepts, and integrates as a module into deep-learning forecasting backbones.
CORAL: Concept Drift Representation Learning for Co-evolving Time-series Kunpeng Xu 1 Lifei Chen 1
CORAL is a method for co-evolving (multivariate, interacting) time series subject to concept drift, the gradual or abrupt change over time in the statistical relationships among the series. It expres…