Editorial: Smart forecasting: deep learning and explainable AI for real-world time series prediction
Abstract
For analytical clarity, the contributions can be organized into four interrelated thematic areas: Smart Forecasting Beyond Finance; Deep Learning and Hybrid Models for Financial Forecasting; Large Language Models for Next-Generation Forecasting; and Perspectives on Intelligent Forecasting. Together, these themes reflect the growing maturity of AI-based forecasting research and its evolution toward increasingly explainable, hybrid, and domain-aware predictive systems.• Smart Forecasting Beyond Finance. Forecasting techniques are increasingly expanding beyond their traditional applications in economics and finance, addressing operational challenges in domains where anticipating future events can significantly improve decision-making. Together, these studies suggest that intelligent forecasting is becoming an increasingly valuable decision-support technology beyond conventional financial applications. They demonstrate how combining deep learning with explainability enables more transparent and actionable predictions in domains where operational planning, resource allocation, and early intervention play a central role.• Deep Learning and Hybrid Models for Financial Forecasting. Financial forecasting remains one of the most dynamic research areas for artificial intelligence, with recent studies increasingly exploring hybrid approaches that combine statistical modeling with modern AI techniques. • Large Language Models for Next-Generation Forecasting. The emergence of large…
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