Results 11 to 20 of about 211,599 (259)
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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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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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
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Multivariate Time Series Forecasting with Transfer Entropy Graph
Tsinghua Science and Technology, 2023 Multivariate Time Series (MTS) forecasting is an essential problem in many fields. Accurate forecasting results can effectively help in making decisions. To date, many MTS forecasting methods have been proposed and widely applied.
Ziheng Duan +4 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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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
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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
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Topological machine learning for multivariate time series [PDF]
Journal of Experimental & Theoretical Artificial Intelligence, 2021 18 pages, to appear in Journal of Experimental & Theoretical Artificial ...
Chengyuan Wu, Carol Anne Hargreaves
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Review of Multivariate Time Series Clustering Algorithms [PDF]
Jisuanji kexue yu tansuoMultivariate time series (MTS) data, serving as a crucial basis for intelligent technologies across numerous domains, record the state changes of multiple variables in systems over time.
ZHENG Desheng, SUN Hanming, WANG Liyuan, DUAN Yaoxin, LI Xiaoyu
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