Results 11 to 20 of about 234,460 (307)
Multivariate semi-blind deconvolution of fMRI time series
NeuroImage, 2021 Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition.
Hamza Cherkaoui +4 more
doaj +1 more source
Multivariate time series classification using kernel matrix
Electronics Letters, 2022 Multivariate time series (MTS) classification is a fundamental problem in time series mining, and the approach based on covariance matrix is an attractive way to solve the classification. In this study, it is noted that a traditional covariance matrix is
Jiancheng Sun +4 more
doaj +1 more source
Multivariate Count Data Models for Time Series Forecasting
Entropy, 2021 Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from
Yuliya Shapovalova +2 more
doaj +1 more source
Network-based segmentation of biological multivariate time series. [PDF]
PLoS ONE, 2013 Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in ...
Nooshin Omranian +3 more
doaj +1 more source
Topological Data Analysis for Multivariate Time Series Data
Entropy, 2023 Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach that can deal with various data modalities of varying complexities.
Anass B. El-Yaagoubi +2 more
doaj +1 more source
On the Number of Signals in Multivariate Time Series [PDF]
, 2018 We assume a second-order source separation model where the observed multivariate time series is a linear mixture of latent, temporally uncorrelated time series with some components pure white noise. To avoid the modelling of noise, we extract the non-noise latent components using some standard method, allowing the modelling of the extracted univariate ...
Markus Matilainen +2 more
openaire +2 more sources
Irregularly Sampled Multivariate Time Series Classification: A Graph Learning Approach [PDF]
, 2023 To date, graph-based learning methods are proven to be effective for modeling spatial and structural dependencies. However, when applied to IS-MTS, they encounter three major challenges due to the complex data characteristics of IS-MTS: 1) variable time ...
Jiang, Ting +9 more
core +1 more source
Explainable AI Framework for Multivariate Hydrochemical Time Series
Machine Learning and Knowledge Extraction, 2021 The understanding of water quality and its underlying processes is important for the protection of aquatic environments. With the rare opportunity of access to a domain expert, an explainable AI (XAI) framework is proposed that is applicable to ...
Michael C. Thrun +2 more
doaj +1 more source
Generalized Network Autoregressive Processes and the GNAR Package
Journal of Statistical Software, 2020 This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new class of generalized network autoregressive processes.
Marina Knight +3 more
doaj +1 more source
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [PDF]
, 2022 Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
Chen, Siheng +11 more
core +2 more sources