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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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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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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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Variable grouping in multivariate time series via correlation [PDF]
, 2001 The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge.
Liu, X, Swift, S, Tucker, A
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Multivariate dynamic kernels for financial time series forecasting [PDF]
, 2016 The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies ...
AJ Smola +6 more
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Control Charts for Multivariate Nonlinear Time Series
Revstat Statistical Journal, 2015 In this paper control charts for the simultaneous monitoring of the means and the variances of multivariate nonlinear time series are introduced. The underlying target process is assumed to be a constant conditional correlation process (cf. [3]).
Robert Garthoff +2 more
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Identifying, exploring, and interpreting time series shapes in multivariate time intervals
Visual Informatics, 2023 We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes. A data structure representing a single episode is a multivariate time series.
Gota Shirato +2 more
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Exploring Dynamic Structures in Matrix-Valued Time Series via Principal Component Analysis
Axioms, 2023 Time-series data are widespread and have inspired numerous research works in machine learning and data analysis fields for the classification and clustering of temporal data. While there are several clustering methods for univariate time series and a few
Lynne Billard +2 more
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Optimal model-free prediction from multivariate time series [PDF]
, 2015 Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for ...
Donner, Reik V. +2 more
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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, Carlos
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