Advances in Time Series Forecasting and Applications in Finance

Time series forecasting is a cornerstone of quantitative finance. At the research frontier, forecasting plays a central role in many areas. Accurate forecasts of returns and risk premia are fundamental for asset pricing, which lies at the core of systematic trading strategies. In risk management, forecasting volatility, tail risk, and correlation dynamics is essential for managing leverage, position sizing, and capital allocation.

This Research Topic invites contributions that address key challenges in time series forecasting for financial applications, such as the development of robust models that capture regime shifts, nonlinear dependencies, cross-asset spillover effects, the use of machine learning techniques to capture complex interactions among features, and the evaluation of forecasting uncertainty and multivariate forecasting, etc.

Topics of interest include (but are not limited to):
- deep learning forecasting architectures,
- tree-based machine learning methods,
- adaptive signal processing,
- state space models,
- hybrid models,
- conformal prediction,
- hierarchical Bayesian models,
- explainable AI for time series.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: time series forecasting, machine learning, deep learning, adaptive signal processing, empirical asset pricing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.