Financial Time Series Analysis and Forecasting in the Big Data Era

Dear Colleagues,

(1) Introduction, including scientific background and highlighting the importance of this research area.

Financial time series analysis and forecasting are foundational to modern finance, enabling predictions of stock prices, volatility, and different economic indicators. In the big data era, traditional models like ARIMA, GARCH, and VAR have proven insufficient for capturing the complex nonlinear dynamics and long-range dependencies inherent in contemporary data structures.

Recent advanced algorithms in artificial intelligence, machine learning, deep learning, and high-performance big data technologies have made it possible to collect and process massive streams of both structured and unstructured financial data. These innovations provide novel insights into complex market relationships that extend far beyond the capabilities of classical models. As global markets become increasingly fragmented and volatile, advanced time series analysis in the era of digital economy serves as a critical tool for financial risk mitigation, portfolio optimization, and strategic decision-making.

(2) Aim of the Special Issue and how the subject relates to the journal scope.

This Special Issue aims to present high-quality research contributions that advance methodologies, applications, and the theoretical understanding of financial time series analysis and forecasting in the context of big data analytics and computing. We seek submissions that propose novel algorithms, architectural frameworks, or empirical studies designed to handle large-scale temporal financial data.

We welcome studies utilizing contemporary techniques such as deep learning, real-time data stream processing, and high-performance computing. We encourage submissions that bridge the gap between computational complexity and financial interpretability to solve high-stakes challenges in the modern economy.

(3) Suggest themes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

• Agentic AI & Autonomous Systems: The role of autonomous agents in navigating multi-agent financial environments and market-making.
• Alternative Data and Sentiment Ingestion: Processing unstructured data (news, social media, satellite data) via NLP and LLMs for market prediction.
• Architectures for Large-Scale Forecasting: Development of Transformer-based models, GNNs, and hybrid architectures for high-dimensional financial data.
• Real-Time Data Stream Processing: Computing frameworks designed for low-latency forecasting in high-frequency trading.
• Risk and Volatility Modeling: Advanced techniques for forecasting systemic risk and market stress in fragmented environments.
• Optimization and Decision Support: Integrating forecasting outputs into deep reinforcement learning for automated portfolio management.
• Quantum-Enhanced Forecasting: Exploratory research on the potential of quantum computing to solve complex portfolio optimization and price prediction problems.

We look forward to receiving your contributions.

Dr. Cuong Nguyen
Dr. Bo Qin
Guest Editors

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