Continual Learning Architectures for Non-Stationary Financial Time Series Prediction
Keywords:
Continual learning, non-stationary time series, financial prediction systems, adaptive machine learning, model drift, financial AI infrastructure, catastrophic forgetting, algorithmic trading systems, machine learning operations, socio-technical systemsAbstract
Financial time series prediction under non-stationary conditions remains one of the most challenging problems in modern computational finance and machine learning systems. The inherent volatility of markets, driven by structural breaks, evolving macroeconomic regimes, behavioral feedback loops, and exogenous shocks, renders traditional static learning paradigms insufficient for sustained predictive performance. Continual learning has emerged as a promising paradigm for addressing such challenges by enabling models to incrementally adapt to new data distributions without catastrophic forgetting of prior knowledge. This paper provides a comprehensive systems-level examination of continual learning architectures designed for non-stationary financial time series prediction. We analyze architectural paradigms including replay-based systems, regularization-driven adaptation frameworks, dynamic expansion networks, and hybrid memory-augmented designs. Emphasis is placed on the operational constraints of financial deployment environments, including latency sensitivity, regulatory compliance, interpretability requirements, and infrastructural scalability. The paper further explores the socio-technical dimensions of continual learning systems in financial contexts, highlighting governance structures, fairness considerations, and risk propagation mechanisms. Through a synthesis of architectural trade-offs and deployment realities, we argue that robust financial prediction systems must move beyond purely model-centric innovation toward integrated system architectures that co-evolve with market dynamics. The study concludes with a discussion of emerging directions in adaptive model orchestration, decentralized learning infrastructures, and policy-aware machine learning systems for financial ecosystems.
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