Prediction and Inference in a Partially Hidden Markov-switching Framework with Autoregression. Application to Machinery Health Diagnosis. - Fatoumata Dama; Christine Sinoquet (ICTAI, 2021)

Partially Hidden Markov Chain Linear Autoregressive model: inference and forecasting. - Fatoumata Dama; Christine Sinoquet (CoRR, 2021)

Analysis and modeling to forecast in time series: a systematic review. - Fatoumata Dama; Christine Sinoquet (CoRR, 2021)

A Framework for Context-Sensitive Prediction in Time Series - Feasibility Study for Data-Driven Simulation in Medicine. - Fatoumata Dama; Christine Sinoquet; Corinne Lejus-Bourdeau (DSAA, 2023)

A hidden Markov model with Hawkes process-derived contextual variables to improve time series prediction. Case study in medical simulation. - Fatoumata Dama; Christine Sinoquet; Corinne Lejus-Bourdeau (ESANN, 2023)

A Novel Incremental Learning Strategy Based on Synthetic Data Generated from a Random Forest. - Jordan Gonzalez; Fatoumata Dama; Laurent Cervoni (KI, 2023)

Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting - application to machine health prognostics. - Fatoumata Dama; Christine Sinoquet (Mach. Learn., 2023)

Introduire l'IA dans la lutte contre la fraude - Comment choisir et convaincre ? - Fatoumata Dama; Rita Sleiman; Steve Bellart (APIA, 2024)