Publication
Frequency domain theory for functional time series: Variance decomposition and an invariance principle
Related publications (48)
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A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...
EPFL2024Sparsely Observed Functional Time Series: Theory and Applications
Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...
EPFL2021Travel Time Prediction for Congested Freeways With a Dynamic Linear Model
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Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021Some first results on the consistency of spatial regression with partial differential equation regularization
We study the consistency of the estimator in spatial regression with partial differential equa-tion (PDE) regularization. This new smoothing technique allows to accurately estimate spatial fields over complex two-dimensional domains, starting from noisy ob ...
2020Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces
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We consider the problem of estimating the slope function in a functional regression with a scalar response and a functional covariate. This central problem of functional data analysis is well known to be ill-posed, thus requiring a regularised estimation p ...
2019Force Characterization Of Meso-Scale Haptic Devices
Jamie Paik, Sagar Dattatray Joshi, Frédéric Henri Vaskin Giraud
Recent advances with haptic devices have shown great potential in physically simulating contact with an object. In meso-scale (1 to 10 cm) haptic devices, although the actuation level can be controlled, their small form-factor makes it difficult to measure ...
2019Spatially Varying Monte Carlo SURE for the Regularization of Biomedical Images
Jean-Philippe Thiran, Marco Pizzolato
Regularization, filtering, and denoising of biomedical images requires the use of appropriate filters and the adoption of efficient regularization criteria. It has been shown that the Stein’s Unbiased Risk Estimate (SURE) can be used as a proxy for the mea ...
IEEE2019Patching large missing gaps of dissolved oxygen data in an intermittent stream: comparison of interpolation techniques, including machine learning models
Eco-hydrologicalmodels are useful tools for water qualitymanagement, but there implementation may require high-resolution boundary condition data which are often patchy in time due to monitoring costs. In this report, we compare the performance of gradient ...
2019Local estimation of the global horizontal irradiance using an all-sky camera
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Localised and high-frequency measurements of the global horizontal irradiance (GHI) are a key information to assess the level of power production of distributed photovoltaic generation. The paper presents a supervised machine learning-based procedure to es ...
PERGAMON-ELSEVIER SCIENCE LTD2018The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems
Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara
Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...
2017