189 citations · 888 across the 107 of their papers we have counts for
112 papers
Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series
Ziling Ma, Junshu Jiang, Ángel López-Oriona +2
We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The central idea is to learn an adaptive…
Partial Wavelet Canonical Coherence for Nonstationary Signals with High Dimensional Confounders
Haibo Wu, Marina I. Knight, Hernando Ombao
We develop Partial Wavelet Canonical Coherence for measuring the direct canonical association between two multivariate nonstationary time series after adjustment for possibly high-…
Topological Effective Connectivity Modeling in Brain Networks
Anass El-Yaagoubi, Moo K. Chung, Hernando Ombao
Characterizing directed information flow in brain networks is difficult because neural circuits are full of recurrent feedback loops. Many existing tools for directed dependence as…
Vector Space of Cycles
Moo K. Chung, Anass B. El-Yaagoubi, Hernando Ombao
Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represent feedback primarily through…
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
Hwa Hui Tew, Junn Yong Loo, Fang Yu Leong +6
Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the…
Causality as a Minimum Energy Principle
Moo K. Chung, D. Vijay Anand, Anass B El-Yaagoubi +3
Classical causal models, such as Granger causality and structural equation modeling, are largely restricted to acyclic interactions and struggle to represent cyclic and higher-orde…
Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework
Ziling Ma, Ángel López Oriona, Hernando Ombao +1
We study adaptive pooling under predictive heterogeneity in high-dimensional multivariate time series forecasting, where global models improve statistical efficiency but may fail t…
Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals
Mara Sherlin Talento, Jordan Richards, Raphael Huser +1
We develop a novel characterization of extremal dependence between two cortical regions of the brain when its signals display extremely large amplitudes. We show that connectivity…
Nonlinear Coherence for Vector Time Series: Defining Region-to-Region Functional Brain Connectivity
Paolo Victor Redondo, Raphaël Huser, Hernando Ombao
Alterations in functional brain connectivity characterize neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD). As a non-invasive and cost…
Robust fuzzy clustering for high-dimensional multivariate time series with outlier detection
Ziling Ma, Ángel López-Oriona, Hernando Ombao +1
Fuzzy clustering provides a natural framework for modeling partial memberships, particularly important in multivariate time series (MTS) where state boundaries are often ambiguous.…
T2I-Diff: fMRI Signal Generation via Time-Frequency Image Transform and Classifier-Free Denoising Diffusion Models
Hwa Hui Tew, Junn Yong Loo, Yee-Fan Tan +5
Functional Magnetic Resonance Imaging (fMRI) is an advanced neuroimaging method that enables in-depth analysis of brain activity by measuring dynamic changes in the blood oxygenati…
EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
Mohammad Mehedi Hasan, Pedro G. Lind, Hernando Ombao +2
Dementia (DEM) is a growing global health challenge, underscoring the need for early and accurate diagnosis. Electroencephalography (EEG) provides a non-invasive window into brain…
Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram
Ziling Ma, Mara Sherlin Talento, Ying Sun +1
Clustering multivariate time series (MTS) is challenging due to non-stationary cross-dependencies, noise contamination, and gradual or overlapping state boundaries. We introduce a…
Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?
Malik Shahid Sultan, Hernando Ombao, Maurizio Filippone
Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Vector autoregressive models (VAR) are simple and eas…
Filtration-Based Learning of Multiscale Shared Structures for Multiple Functional Predictors
Shuhao Jiao, Hernando Ombao, Ian W. McKeague
It is crucial to learn the shared structures among functional predictors, as these structures characterize how predictor components exert common effects and, more generally, how pr…
FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis
Ziling Ma, Ángel López-Oriona, Hernando Ombao +1
Clustering multivariate time series data is a crucial task in many domains, as it enables the identification of meaningful patterns and groups in time-evolving data. Traditional ap…
Wavelet Canonical Coherence for Nonstationary Signals
Haibo Wu, Marina I. Knight, Keiland W. Cooper +2
Understanding the evolving dependence between two clusters of multivariate signals is fundamental in neuroscience and other domains where sub-networks in a system interact dynamica…
From Density to Void: Why Brain Networks Fail to Reveal Complex Higher-Order Structures
Moo K. Chung, Anass B. El-Yaagoubi, Anqi Qiu +1
In brain network analysis using resting-state fMRI, there is growing interest in modeling higher-order interactions beyond simple pairwise connectivity via persistent homology. Des…
Spectral Extremal Connectivity of Two-State Seizure Brain Waves
Mara Sherlin D. Talento, Jordan Richards, Marco Pinto-Orellana +2
Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading…
SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation
Yee-Fan Tan, Jun Lin Liow, Pei-Sze Tan +4
Modern brain imaging technologies have enabled the detailed reconstruction of human brain connectomes, capturing structural connectivity (SC) from diffusion MRI and functional conn…
KenCoh: A Ranked-Based Canonical Coherence
Mara Sherlin D. Talento, Sarbojit Roy, Tania Reyes Vallejo +2
This work is inspired by the problem of characterizing a dependence measure between two cortical regions of the brain where each region contains multiple signal recordings from sev…
A Robust Topological Framework for Detecting Regime Changes in Multi-Trial Experiments with Application to Predictive Maintenance
Anass B. El-Yaagoubi, Jean-Marc Freyermuth, Hernando Ombao
We present a general and flexible framework for detecting regime changes in complex, non-stationary data across multi-trial experiments. Traditional change point detection methods…
Granger Causality for Mixed Time Series Generalized Linear Models: A Case Study on Multimodal Brain Connectivity
Luiza S. C. Piancastelli, Wagner Barreto-Souza, Norbert J. Fortin +2
This paper is motivated by studies in neuroscience experiments to understand interactions between nodes in a brain network using different types of data modalities that capture dif…
Nonlinear Causality in Time Series Networks: With Application to Motor Imagery vs Execution
Sipan Aslan, Hernando Ombao
Causal interactions in time series networks can be dynamic and nonlinear, making it difficult to identify them using conventional linear causality estimations. We propose a novel a…