Integrative Approaches To Risk Forecasting And Management In Complex Systems Using Advanced Time-Series And AI-Driven Models

Abstract

The contemporary landscape of risk forecasting and management is increasingly shaped by the convergence of complex data streams, digital technologies, and artificial intelligence (AI) methodologies. This study synthesizes theoretical frameworks and applied methodologies for predicting and mitigating risks across financial, industrial, and environmental systems. Time-series modeling, particularly advanced neural network architectures such as Long Short-Term Memory (LSTM) and Nonlinear Auto-Regressive with Exogenous Input (NARX), forms the core analytical tool for forecasting dynamic events in volatile environments (Lim & Zohren, 2021; Amelot et al., 2021). Complementary techniques, including fuzzy association rules, hybrid kernel principal component analysis-support vector regression (KPCA–SVR), and attention-based architectures, are explored for their efficacy in capturing non-linear relationships and temporal dependencies in large-scale datasets (Shang et al., 2021; Li et al., 2021).

The study emphasizes the role of digital transformation, Industry 4.0 integration, and real-time data pipelines, such as Kafka event sourcing, in enabling responsive and adaptive risk analytics (Kesarpu & Dasari, 2025; Ivanov et al., 2019). Multidimensional applications are considered, ranging from financial risk evaluation, foreign exchange forecasting, and industrial operational monitoring to natural hazard estimation, including landslides and flood volume projections (Mesioye & Bakare, 2020; Kwak et al., 2013; Dai et al., 2002). Critical challenges such as uncertainty quantification, data quality assessment, and the ethical implementation of AI-driven decision systems are also discussed (Walker et al., 2003; Aven, 2016).

This research integrates theoretical and applied perspectives to propose a unified framework for risk prediction, emphasizing methodological rigor, model interpretability, and actionable insights. The findings highlight that leveraging deep learning, hybrid modeling, and event-driven analytics not only improves forecasting accuracy but also enhances proactive decision-making in dynamic and uncertain environments. The study concludes with a forward-looking agenda that identifies emerging trends in AI-assisted risk management, including the potential for scalable, near real-time monitoring systems across diverse sectors.