2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun +1
In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We pro…
GRAVITY: A Controversial Graph Representation Learning for Vertex Classification
Etienne Gael Tajeuna, Jean Marie Tshimula
In the quest of accurate vertex classification, we introduce GRAVITY (Graph-based Representation leArning via Vertices Interaction TopologY), a framework inspired by physical syste…
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions
René Manassé Galekwa, Jean Marie Tshimula, Etienne Gael Tajeuna +1
The sports betting industry has experienced rapid growth, driven largely by technological advancements and the proliferation of online platforms. Machine learning (ML) has played a…
Modeling Regime Shifts in Multiple Time Series
Etienne Gael Tajeuna, Mohamed Bouguessa, Shengrui Wang
We investigate the problem of discovering and modeling regime shifts in an ecosystem comprising multiple time series known as co-evolving time series. Regime shifts refer to the ch…