Our Scientific Publications
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.
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.
