9 papers
Day-Ahead Electricity Price Forecasting Using Merit-Order Curves Time Series
Guillaume Koechlin, Filippo Bovera, Piercesare Secchi
We introduce a general, simple, and computationally efficient functional data analysis framework for forecasting day-ahead supply and demand merit-order curves, and the resulting e…
Understanding Long-Term Dynamics of Individual Metro Usage: A Hidden Semi-Markov State Framework with Survival Analysis
Bingxun Wang, Valeria Maria Urbano, Shan He +4
Understanding how individual metro usage evolves over multi-year horizons is essential for transit planning and passenger retention. However, existing approaches typically characte…
A Convolution Process for Sea Surface Temperature Hot-Spot Identification in the Mediterranean Sea
Leonardo Marchesin, Alessandra Menafoglio, Piercesare Secchi
Sea surface temperature (SST) is a fundamental determinant of global climate dynamics and economic activity. Reliable projections of future SST patterns depend critically on a rigo…
A Blind Source Separation Framework to Monitor Sectoral Power Demand from Grid-Scale Load Measurements
Guillaume Koechlin, Filippo Bovera, Elena Degli Innocenti +4
As demand-side flexibility becomes increasingly necessary to integrate variable renewable energy, understanding electricity demand composition across different grid levels is essen…
Fixed Rank co-Kriging: a model for multivariate spatial prediction
Gaia Caringi, Piercesare Secchi
This work develops a multivariate extension of the Fixed Rank Kriging (FRK) framework for spatial prediction in settings where multiple spatial processes may provide complementary…
Unraveling time-varying causal effects of multiple exposures: integrating Functional Data Analysis with Multivariable Mendelian Randomization
Nicole Fontana, Francesca Ieva, Luisa Zuccolo +2
Mendelian Randomization is a widely used instrumental variable method for assessing causal effects of lifelong exposures on health outcomes. Many exposures, however, have causal ef…