ICAIF 2025 Workshop | Rethinking Financial Time-Series

Call for Papers

Important Dates

(UTC-0)

Submission Deadline:

October 2nd, 2025
Author Notification:

October 15th, 2025
Camera-Ready Deadline:

October 31st, 2025
Workshop Date:

Nov 15th or 16th, 2025

Submit via OpenReview

Submission Guidelines

Authors must submit their paper (as a PDF) via the workshop submission site. At least one author of each accepted paper must attend the conference to present their work.

Format and Awards

Submissions are limited to 4 pages in length, excluding references and appendices. The paper format should be the same as the main ICAIF conference. We will be awarding a Best Paper Award to recognize outstanding contributions.

Review Process

The review process will be double-blind. There will be no rebuttal period.

Presentation and Proceedings

All accepted papers will be invited to a poster session. Participants are required to print and bring their own posters to the event. Detailed information regarding poster format and submission requirements will be provided by the organizers upon acceptance. Selected papers may also be given the opportunity for an oral presentation, subject to schedule constraints. The workshop is non-archival, and there will be no official proceedings. Only the names of the authors and the titles of the accepted papers will be posted on the website; the papers themselves will not be made public.

Invited Speakers

The list of confirmed speakers will be announced soon. Stay tuned!

Topics of Interest

Foundational Time-Series Principles

Principled time-series methods for heavy-tails, volatility clustering, and regime shifts.
Post-mortems of time-series model failures in live trading.
Robust benchmarks and backtesting protocols for financial time-series.
High-fidelity synthetic time-series generation (e.g., GANs, SDEs).

Frontiers in Time-Series Data & Models

Modeling non-stationary, multi-modal, and irregularly-sampled time-series.
Online learning and adaptation to distribution shifts in time-series.
Foundation models for financial time-series: Scaling laws and limitations.
Self-supervised representation learning for time-series.

New Paradigms for Time-Series Analysis

Causal discovery and inference from observational time-series data.
Interpretability of deep learning models for time-series forecasting (XAI).
Rigorous uncertainty quantification for probabilistic time-series forecasting.
Integrating market microstructure into time-series model design.

Tentative Schedule

08:00 - 08:05

Opening Remarks

08:05 - 08:35

Keynote #1

08:35 - 09:05

Invited Talk #1

09:05 - 09:45

Best Papers Talks (#1-#2)

09:45 - 10:00

Coffee Break

10:00 - 11:20

Invited Talks & Best Paper

Invited Talk #2, Invited Talk #3 (Industry), Best Papers Talk #3

11:20 - 11:55

Keynote #2 & Closing Remarks

11:55 onwards

Networking & Poster Session

Organizers

Yoontae Hwang

University of Oxford

Yongjae Lee

UNIST

Zhangyang (Atlas) Wang

UT Austin

Qingsong Wen

Squirrel AI Learning

Ming Jin

Griffith University

Stefan Zohren

University of Oxford

Dhagash Mehta

BlackRock, Inc.

Yaxuan Kong

University of Oxford

Changhee Lee

Korea University

Jaeho Kim

Korea University

Hao Ni

University College London

About the Workshop

Financial time-series analysis sits at the epicentre of today's algorithmic markets. However, its core foundations remain unsettled; the heavy-tailed noise, abrupt regime shifts, and market-microstructure frictions inherent to financial data continue to defy standard modelling assumptions. Ignoring these fundamentals has led to models that are often brittle in practice.

Simultaneously, the field is being reshaped by new frontiers in data and modelling. The proliferation of cross-modal signals from millisecond order-book updates and satellite feeds to generative-AI-curated news is overwhelming traditional pipelines. This data deluge has spurred the rise of large-scale foundation models, challenging us to reconcile immense scale with statistical soundness and avoid creating sophisticated yet unreliable black boxes.

This workshop provides a forum to confront these questions head-on. We solicit contributions that re-evaluate foundational principles, push the frontiers of model development, or chart future research paths. We welcome submissions on principled learning for volatile data, robustness and distribution shifts, novel benchmarks and evaluation protocols, case studies of model failures, and methods for causal inference or interpretability.