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Data analysis
Related publications (1,000)
On distributional autoregression and iterated transportation
Victor Panaretos, Laya Ghodrati
We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...
Hoboken2024Spectral Estimators for High-Dimensional Matrix Inference
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 ...
EPFL2024Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions
Florent Evariste Forest, Yunhong Che
Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...
Ieee-Inst Electrical Electronics Engineers Inc2024High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...
2024Partial discharge localization in power transformer tanks using machine learning methods
Marcos Rubinstein, Hamidreza Karami
This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...
2024Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets
Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...
Lausanne2024Intraday solar irradiance forecasting using public cameras
Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini
With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...
Pergamon-Elsevier Science Ltd2024A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
Daniel Kuhn, Yves Rychener, Viet Anh Nguyen
The state-of-the-art methods for estimating high-dimensional covariance matrices all shrink the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage target. The underlying shrinkage transformation is either chosen heuristically ...
2024Novel theory and potential applications of central diastolic pressure decay time constant
Nikolaos Stergiopoulos, Georgios Rovas, Sokratis Anagnostopoulos, Vasiliki Bikia, Patrick Segers
Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...
2024Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems
In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...
EPFL2024