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Joint Fusion Learning of Multiple Time Series Prediction

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Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Ekaterina Krymova, Nicola Parolini, Andrea Kraus, David Kraus, Daniel Lopez, Markus Scholz, Tao Sun

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

EPFL2023

Multivariate time series forecasting for freeway networks

Semin Kwak

This dissertation introduces traffic forecasting methods for different network configurations and data availability.Chapter 2 focuses on single freeway cases.Although its topology is simple, the non-linearity of traffic features makes this prediction still ...

EPFL2022

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Activity-based modeling and simulation of epidemics

Michel Bierlaire, Cloe Cortes Balcells, Rico Krüger

The SARS-CoV-2 outburst in March 2020 has led to the lockdown of several countries across the world. Mobility restrictions have been constantly put into action and reversed to find the trade-off between minimizing the number of infections and death and mit ...

2021

The new era of sharing information and "big data" has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the e ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Demand forecasting for a novel transportation mode

Denis Aloyse Joseph Steffen

The vehicle sharing systems (VSSs) are becoming more and more popular due to both economic and environmental effects. However, these systems mainly focus on two specific types of vehicles: bicycles and cars. Recently, some other types of vehicles (e.g. lig ...

2019

Dynamic Clustering and Control for Large Scale Urban Networks

Mohammadreza Saeedmanesh

Traffic congestion is a significant issue in all urban areas with concentration of activities for various city topologies and distribution of population and land use around the world. Developing realistic models that are able to replicate congestion spread ...

EPFL2018

, ,

Flood forecasting systems are today recognized as a key element in natural hazard mitigation. The objective is to exploit the available observed and forecasted meteorological information to foresee river discharges up to several days in advance, using a hy ...

2016

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Michel Bierlaire, Anna Fernández Antolín

Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using r ...

2015