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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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Learning short multivariate time series models through evolutionary and sparse matrix computation [PDF]
, 2005 Multivariate time series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is important for many decision making activities.
Liu, X, Kok, J, Swift, S
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The Arrow of Time in Multivariate Time Series
, 2016 We prove that a time series satisfying a (linear) multivariate autoregressive moving average (VARMA) model satisfies the same model assumption in the reversed time direction, too, if all innovations are normally distributed. This reversibility breaks down if the innovations are non-Gaussian.
Bauer, S., Schölkopf, B., Peters, J.
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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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Outlier detection in multivariate time series via projection pursuit [PDF]
, 2004 This article uses Projection Pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions could be more powerful than testing the multivariate series directly.
Peña, Daniel +2 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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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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A Bayesian network approach to explaining time series with changing structure [PDF]
, 2004 Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures.
Liu, X, Tucker, A
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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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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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