Results 11 to 20 of about 794,789 (182)
Multiview Spatial-Temporal Meta-Learning for Multivariate Time Series Forecasting [PDF]
SensorsMultivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving ...
Liang Zhang +3 more
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4D hypercomplex-valued neural network in multivariate time series forecasting [PDF]
Scientific ReportsThe goal of this paper is to test three classes of neural network (NN) architectures based on four-dimensional (4D) hypercomplex algebras for multivariate time series forecasting.
Radosław Kycia, Agnieszka Niemczynowicz
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Consistency regularization for few shot multivariate time series forecasting [PDF]
Scientific ReportsMultivariate time series forecasting aims to accurately predict future trends by capturing and analyzing various features of the time series. Adequate training data are crucial for ensuring the model’s generalizability.
Yumei She +5 more
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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
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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
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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
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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
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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
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Graphical modelling of multivariate time series [PDF]
, 2011 We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear ...
Eichler, Michael
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Skip-RCNN: A Cost-Effective Multivariate Time Series Forecasting Model
IEEE Access, 2023 Multivariate time series (MTS) forecasting is a crucial aspect in many classification and regression tasks. In recent years, deep learning models have become the mainstream framework for MTS forecasting. Among these deep learning methods, the transformer
Haitao Song +6 more
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