TL;DR
The results show that pairing lightweight symbolic constraints with drift-triggered continual learning yields causal inference that stays valid as the world changes.
This work views delayed residuals as compressed observations of latent drift over the horizon, and proposes PADRE, a drift evidence driven framework that converts each delayed feedback event into a context-sensitive adaptation decision, and builds a Tri-View drift representation.
The Regime-Enhanced Intelligent Granger Network is proposed, a novel causal discovery framework specifically engineered for nonstationary time series and achieves an Optimal F1 score of 0.529, substantially outperforming PCMCI+ and PC in structural edge recovery.
ReCATS combines multi-regime modeling with a dual-phase generative alignment mechanism, enabling effective adaptation to distribution shifts while preserving knowledge acquired from past tasks, and introduces a dynamic dual-scale thresholding strategy to mitigate decision boundary drift under evolving data distribution...
Time-series anomaly detection in real-world streams is often challenged by evolving operating conditions, where distribution shifts can be easily mistaken for anomalies. Due to this, we study a new problem, online latent-domain anomaly detection, where domain labels and shift times are unobserved, the number of domains...
This work proposes a taxonomy that categorizes drift events by their causal origin, including changes in exogenous variables, endogenous mechanisms, confounders, and target-generating processes, and develops an SCM-based data stream generator that simulates controlled mechanism-level drift events.
Operational-risk early-warning models in financial institutions operate under nonstationarity, extreme class imbalance, delayed outcome confirmation, and finite investigation capacity. This paper proposes DARC-OR, a drift-response framework that combines an immediately available Jensen-Shannon divergence signal on mode...
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