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Clustering of multivariate time-series data [PDF]
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002 A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is
Ashish Singhal, Dale E. Seborg
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Nonparametric frequency domain analysis of nonstationary multivariate time series [PDF]
, 2002 We analyse the properties of nonparametric spectral estimates when applied to long memory and trending nonstationary multiple time series. We show that they estimate consistently a generalized or pseudo-spectral density matrix at frequencies both close ...
Velasco Gómez, Carlos +2 more
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Inferring causal relations from multivariate time series : a fast method for large-scale gene expression data [PDF]
, 2009 Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally ...
Yuan, Yinyin +3 more
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Applied SciencesWeather forecasting is essential for various applications such as agriculture and transportation, and relies heavily on meteorological sequential data such as multivariate time series collected from weather stations.
Zhengrui Wang +3 more
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Multivariate time series prediction based on ARCLSTM
Journal of Measurement Science and Instrumentation, 2021 Time series is a kind of data widely used in various fields such as electricity forecasting, exchange rate forecasting, and solar power generation forecasting, and therefore time series prediction is of great significance.
QIAO Gangzhu, SU Rong, ZHANG Hongfei
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Network structure of multivariate time series [PDF]
Scientific Reports, 2015 AbstractOur understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing.
Lacasa L +2 more
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Goodness-of-Fit Tests for Copulas of Multivariate Time Series
Econometrics, 2017 In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural
Bruno Rémillard
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Monitoring multivariate time series
Journal of Multivariate Analysis, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Online Clustering of Multivariate Time-series [PDF]
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016 Copyright © by SIAM. The intrinsic nature of streaming data requires algorithms that are capable of fast data analysis to extract knowledge. Most current unsupervised data analysis techniques rely on the implementation of known batch techniques over a sliding window, which can hinder their utility for the analysis of evolving structure in applications ...
Masud Moshtaghi +2 more
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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. The unobservable, nonstationary factors are identified by expanding the
Qiwei Yao +3 more
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