Data Science Research Group: Nowcasting and Forecasting

Nowcasting and Forecasting

Description

One of our focus areas is nowcasting and forecasting, with emphasis on sequential and temporal data. Using Big Data deriving from everyday life as external proxies, it is possible to nowcast and forecast the evolution of phenomena whose study relies only on historical data or data that come with a significant lag. We work mainly on epidemics, healthcare, peace, and sentiment.

Latest publications

	Understanding peace through the world news Voukelatou, Vasiliki, Miliou, Ioanna, Giannotti, Fosca, and Pappalardo, Luca EPJ Data Science, 2022
	Impact of Dimensionality on Nowcasting Seasonal Influenza with Environmental Factors Guarnizo, Stefany, Miliou, Ioanna, and Papapetrou, Panagiotis In International Symposium on Intelligent Data Analysis, 2022
	Sentiment Nowcasting During the COVID-19 Pandemic Miliou, Ioanna, Pavlopoulos, John, and Papapetrou, Panagiotis In International Conference on Discovery Science, 2021
	Predicting seasonal influenza using supermarket retail records Miliou, Ioanna, Xiong, Xinyue, Rinzivillo, Salvatore, Zhang, Qian, Rossetti, Giulio, Giannotti, Fosca, Pedreschi, Dino, and Vespignani, Alessandro PLOS Computational Biology, 2021
	Estimating countries’ peace index through the lens of the world news as monitored by GDELT Voukelatou, Vasiliki, Pappalardo, Luca, Miliou, Ioanna, Gabrielli, Lorenzo, and Giannotti, Fosca In 2020 IEEE 7th international conference on data science and advanced analytics (DSAA), 2020

EXTREMUM

People

Panagiotis Papapetrou, Professor

sequential and temporal data mining, explainability, healthcare applications

Ioanna Miliou, Senior Lecturer

nowcasting and forecasting, data science for social good with applications in healthcare, epidemics and peace

Tim Kreuzer