Raphaël Minato
Transformer-based robust predictive models for financial temporal series with concept drift.
Seong-Woo Ahn
Developing an integrative methodological approach for identifying causal microbiome-health relationships in humans.
Decision trees with complex conditions for trading
Decision trees with complex conditions for trading.
Stream graphs for fraud detection
Stream graphs applied to fraud detection.
Enhancing Trading Strategy Robustness through Anomaly Detection
This project explores the use of anomaly detection techniques, such as One-Class SVM, to identify the conditions under which algorithmic trading strategies are likely to succeed, ensuring their applicability and extending the approach to portfolio-wide strategy optimization.
Graph-Based and Time-Series Hybrid Modeling for Fraud Detection
This project develops a hybrid fraud detection model by combining graph-based learning and time-series analysis to capture complex transaction relationships, aiming to enhance fraud prediction accuracy while minimizing false positives in large-scale payment datasets.
Hopular applied to Fraud Detection
Hopular applied to Fraud Detection.