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Concept

Vector autoregression

Related publications (22)

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 ...

Wiley2024

Sparse autoregressive neural networks for classical spin systems

Giuseppe Carleo, Dian Wu, Indaco Biazzo

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Iop Publishing Ltd2024

Comparison of Three Imputation Methods for Groundwater Level Timeseries

Andrea Rinaldo

This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...

MDPI2023

Comparing end-tidal CO2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI

Stefano Moia

Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Indaco Biazzo

Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physic ...

Nature Portfolio2023

Distributional Regression and Autoregression via Optimal Transport

Laya Ghodrati

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

EPFL2023

Modeling Structured Data in Attention-based Models

Alireza Mohammadshahi

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

EPFL2023

On the rate of convergence for the autocorrelation operator in functional autoregression

Victor Panaretos, Alessia Caponera

We consider the problem of estimating the autocorrelation operator of an autoregressive Hilbertian process. By means of a Tikhonov approach, we establish a general result that yields the convergence rate of the estimated autocorrelation operator as a funct ...

ELSEVIER2022

Daily Contributors of Tinnitus Loudness and Distress: An Ecological Momentary Assessment Study

Patrick Karl Alois Neff

BackgroundTinnitus is a heterogeneous condition which may be associated with moderate to severe disability, but the reasons why only a subset of individuals is burdened by the condition are not fully clear. Ecological momentary assessment (EMA) allows a be ...

FRONTIERS MEDIA SA2022

Learning Bollobas-Riordan Graphs Under Partial Observability

Ali H. Sayed

This work examines the problem of learning the topology of a network (graph learning) from the signals produced at a subset of the network nodes (partial observability). This challenging problem was recently tackled assuming that the topology is drawn acco ...

IEEE2021

Efficient Depth-based Deep Learning Methods for Multi-Party Pose Estimation

Angel Noe Martinez Gonzalez

Human detection and pose estimation are essential components for any artificial system responsive to the presence of humans and that react according to human-centered tasks. Robotic systems are typical examples, for which the body pose represents fine grai ...

EPFL2021

Simultaneous autoregressive models for spatial extremes

Emeric Rolland Georges Thibaud

Motivated by the widespread use of large gridded data sets in the atmospheric sciences, we propose a new model for extremes of areal data that is inspired by the simultaneous autoregressive (SAR) model in classical spatial statistics. Our extreme SAR model ...

WILEY2020

Functional lagged regression with sparse noisy observations

Victor Panaretos, Tomas Rubin

A functional (lagged) time series regression model involves the regression of scalar response time series on a time series of regressors that consists of a sequence of random functions. In practice, the underlying regressor curve time series are not always ...

WILEY2020

Multivariate extremes over a random number of observations

Simone Padoan, Stefano Rizzelli

The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as max ...

WILEY2021

Waste collection inventory routing with non-stationary stochastic demands

Michel Bierlaire, Mohammad Yousef Maknoon, Sacha Varone, Iliya Dimitrov Markov

We solve a rich routing problem inspired from practice, in which a heterogeneous fixed fleet is used for collecting recyclable waste from large containers over a finite planning horizon. Each container is equipped with a sensor that communicates its level ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Simultaneous estimation of phase derivative and phase using parallel Kalman filter implementation

Pramod Rastogi, Rishikesh Dilip Kulkarni

This paper proposes a technique for the simultaneous estimation of interference phase derivative and phase from a complex interferogram recorded in an optical interferometric setup. The complex interferogram is represented as a spatially varying autoregres ...

IOP Publishing2016

Essays in Financial Economics

Christopher Trevisan

This thesis is structured in three chapters, each pertaining to a specific problem in financial economics. The first chapter, titled 'High-Frequency Jump Analysis of the Bitcoin Market' and co-authored with Prof. Olivier Scaillet and Adrien Treccani of the ...

EPFL2016

Multiple phase derivative estimation using autoregressive modeling in holographic interferometry

Pramod Rastogi, Rishikesh Dilip Kulkarni

A novel technique is proposed for the direct and simultaneous estimation of multiple phase derivatives from a deformation modulated carrier fringe pattern in a multi-wave holographic interferometry set-up. The fringe intensity is represented as a spatially ...

IOP Publishing2015

Digital holographic moiré for the direct and simultaneous estimation of strain and slope fields

Pramod Rastogi, Rishikesh Dilip Kulkarni

A novel method is proposed for the direct and simultaneous estimation of multiple phase derivatives corresponding to strain and slope fields from a single moiré fringe pattern in digital holographic moiré. The interference field in a given row/column is a ...

Optical Society of America2014

Expectation-Driven Cycles in the Housing Market: Evidence from Survey Data

Luisa Lambertini

Using a vector-autoregression (VAR) model and data from the University of Michigan Survey of Consumers, we provide evidence on the importance of news and consumers' beliefs for housing-market dynamics and aggregate fluctuations. We document that innovation ...

2013