4 citations · 8 across the 13 of their papers we have counts for
5 papers · 1 filter
Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets
Lei Zan, Charles K. Assaad, Emilie Devijver +1
This paper introduces Regime-aware Constraint-Based and Noise-Based causal discovery with Markov Blankets (RCBNB-MB), a novel causal discovery algorithm for time series that relaxe…
Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting
Morad Laglil, Younes Hlal, Marouane El Hadari +2
Reliable long-horizon time series forecasting is an important yet difficult problem. Trends and seasonality introduce complex temporal structure that challenges learning-based fore…
Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
Morad Laglil, Bertrand Pracca, Emilie Devijver +1
Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecast…
Case Studies of Causal Discovery from IT Monitoring Time Series
Ali Aït-Bachir, Charles K. Assaad, Christophe de Bignicourt +5
Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their pro…
Supervised Categorical Metric Learning with Schatten p-Norms
Xuhui Fan, Eric Gaussier
Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development on categorical data still needs further exploration. In this pap…