FLARE: Factor-Loading-Break-Triggered Adaptive Readout for High-Dimensional Time Series under Regime Shift

Time-series forecasters can degrade sharply when the shared low-rank structure of a high-dimensional panel changes across regimes. Yet common remedies such as per-series instance normalization primarily target marginal distribution shifts and can therefore miss changes in the joint dependence structure. To address this challenge, we formulate regime shifts as low-rank structural breaks, covering both span-changing shifts in the loading space and span-preserving changes in factor strength. Building on this formulation, we introduce FLARE, an online detector that uses a whitened amortized representation and a single operator statistic to jointly capture subspace geometry and factor strength. Each detected break then triggers lightweight readout-only adaptation while the forecasting backbone remains frozen. Theoretically, we establish a weak-factor detectability boundary and a detection-delay guarantee, and show that whitening lowers the detection threshold relative to window-wise PCA. Empirically, FLARE detects weak-factor breaks missed by classical factor detectors, localizes a real market regime shift, and matches the forecasting performance of always-on test-time adaptation at a fraction of the update cost.