Our Scientific Publications
A Survey of Explainable AI Methods for Financial Time Series Forecasting
Pierre-Daniel Arsenault, Jean-Marc Patenaude and Shengrui Wang
Published by the Association for Computing Machinery (ACM 2025)
This survey explores how explainable AI (XAI) techniques—such as SHAP, LIME, and interpretable models like attention-based LSTMs and decision trees—are applied to financial time series forecasting tasks like stock price prediction, volatility analysis, and algorithmic trading. By enhancing transparency and trust, these XAI approaches help financial professionals better understand model decisions, align predictions with market behavior, and support more informed investment strategies
Regime Switching Model with Nonlinear Representation for Discovery and Forecasting Regimes in Financial Markets
Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude and Shengrui Wang
Presented at the Society for Industrial and Applied Mathematics Conference (SIAM 2024)
This paper introduces a new type of regime-switching model that uses nonlinear kernel-based representations to automatically detect and forecast shifts in financial market behavior across multiple time series. Its key innovation lies in uncovering hidden, time-varying patterns without prior knowledge of regimes—making it highly relevant for financial applications like volatility forecasting, risk assessment, and market trend analysis.
A Forecasting Model with Robust and Reduced Redundancy Latent Series
Abdallah Aaraba , Shengrui Wang , and Jean-Marc Patenaude
Presented at the Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD-2024)
This paper introduces a deep learning model that improves time series forecasting by learning compact, noise-resistant representations with minimal redundancy. Its key innovation—a non-contrastive self-supervised learning method that improves accuracy and stability—makes it especially useful in finance for predicting complex, interrelated trends like stock prices or economic indicators.
Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting
Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude, and Shengrui Wang
Presented at the Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD-2024)
This paper introduces a kernel-based self-representation learning method for discovering dynamic regimes in co-evolving time series, enabling more accurate and interpretable multi-step forecasting. In finance, this approach is particularly relevant as it captures nonlinear interactions and regime shifts—such as market volatility or economic cycles—without requiring prior knowledge, enhancing predictive modeling for complex financial systems.
Estimator Correction for the Pickands function and Bayesian Estimator
Kevin Chalifoux
Master’s Thesis in Statistics, University of Montreal 2023
This thesis proposes a correction method and a Bayesian estimator for the Pickands dependence function, improving accuracy and ensuring mathematical validity in modeling extreme events. Applications in finance include modeling joint extreme events such as simultaneous market crashes or co-movements in asset returns, improving risk assessment in portfolio management, and stress testing by accurately capturing tail dependencies between financial instruments. (In French)
A Framework to Detect Causal Relationships in Financial Time Series
Patrick Asante Owusu, Etienne Tajeuna, Jean-Marc Patenaude, Armelle Brun, and Shengrui Wang
Presented at the IEEE International Conference on Data Mining (ICDM 2023)
This paper presents a framework using a temporal bipartite graph to model dependencies in time series. It uses autoregressive models for pattern identification and graph-based learning to capture transitions. The method aims to detect causal relationships in financial time series, comparing its accuracy and execution time with traditional methods like Granger causality and PCMCI. Designed for scalability and interpretability, its potential applications include risk assessment, portfolio management, and market behavior analysis in finance.
Characterizing Financial Market Coverage using AI
Jean Marie Tshimula, D’Jeff K. Nkashama, Patrick Owusu, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza, Armelle Brun, Jean-Marc Patenaude, Shengrui Wang, Belkacem Chikhaoui
Published on ArXiv – 2023
The paper uses AI to analyze thousands of YouTube videos from Bloomberg and Yahoo Finance, extracting financial insights through speech-to-text transcription and natural language processing. This helps identify key market trends, influential entities, and evolving narratives—offering valuable tools for financial analysis and investment decision-making.
Clustering-Based Cross-Sectional Regime Identification for Financial Market Forecasting
Rongbo Chen, Mingxuan Sun, Kunpeng Xu, Jean-Marc Patenaude, and Shengrui Wang
Presented at the International Conference on Database and Expert Systems Applications (DEXA 2022)
This paper presents a new method for forecasting financial markets by identifying different market “regimes” using clustering across multiple time series. It captures nonlinear relationships between assets and adapts to changing conditions, leading to more accurate predictions. This approach helps investors and analysts understand shifts in market behavior, improving portfolio management, risk assessment, and trading strategies.
Dynamic Cross-sectional Regime Identification for Financial Market Prediction
Rongbo Chen, Kunpeng Xun, Jean-Marc Patenaude, Shengrui Wang
Presented at the IEEE Computers, Software and Applications Conference (COMPSAC 2022)
This paper proposes a novel model for financial market prediction that dynamically identifies cross-sectional regimes in multi-time-series data, allowing for the discovery of new market regimes as they emerge, rather than relying on a fixed set of pre-identified regimes. This is highly relevant in finance because it enhances the ability to detect structural market changes—such as those during crises or bubbles—improving prediction accuracy and offering better insights into market behavior through time-varying transition probabilities.
Neural Forecasting at Scale
Philippe Chatigny , Shengrui Wang , Jean-Marc Patenaude , Boris N. Oreshkin
Published on ArXiv – 2021
This paper introduces N-BEATS(P), a scalable and memory-efficient deep learning architecture for univariate time series forecasting that significantly reduces computational costs while maintaining state-of-the-art accuracy. Its relevance in finance lies in its demonstrated ability to perform zero-shot forecasting on financial datasets—such as stocks and ETFs—enabling rapid, cost-effective deployment of predictive models across diverse financial instruments without retraining.
Spatiotemporal neural networks for forecasting financial time series
Philippe Chatigny, Jean-Marc Patenaude, Shengrui Wang
Published in the International Journal of Approximate Reasoning – Dec 2020
This paper proposes STANN, a spatiotemporal adaptive neural network that improves long-term forecasting of multivariate financial time series by dynamically adjusting its autoregressive order using an attention mechanism. Applied to investment fund data, STANN significantly enhances forecasting accuracy and supports more effective autonomous trading strategies in finance.
Financial Time Series Representation Learning
Philippe Chatigny, Jean-Marc Patenaude and Shengrui Wang
Published on ArXiv – 2020
This paper introduces an unsupervised deep generative model that improves financial time series forecasting by learning latent representations and inter-series relationships using a dynamic attention mechanism. This approach significantly enhances the accuracy of multi-asset forecasts, making it valuable for financial applications like ETF and mutual fund trajectory prediction.
A Variable-Order Regime Switching Model to Identify Significant Patterns in Financial Markets
Philippe Chatigny, Rongbo Chen, Jean-Marc Patenaude and Shengrui Wang
Presented at the IEEE International Conference on Data Mining (ICDM 2018)
This paper presents t a variable-order regime switching framework that identifies and predicts financial market regimes by extracting statistically significant behavioral patterns from time series data. Applied to volatility forecasting of 200 S&P 500 stocks, the model outperforms traditional regime-switching methods by offering interpretable insights into market dynamics and improving predictive accuracy.
