Results 11 to 20 of about 18,233,102 (295)
Multivariate Time Series Similarity Searching [PDF]
The Scientific World Journal, 2014 Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS.
Jimin Wang +4 more
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Forecasting time series with multivariate copulas [PDF]
Dependence Modeling, 2015 Abstract In this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies, as well as the structure of the dependence.
Simard Clarence, Rémillard Bruno
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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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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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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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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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Modelling multiple time series via common factors [PDF]
, 2008 We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series.
Yao, Qiwei, Pan, Jiazhu
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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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