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Comparing climate time series – Part 2: A multivariate test [PDF]
Advances in Statistical Climatology, Meteorology and Oceanography, 2021 This paper proposes a criterion for deciding whether climate model simulations are consistent with observations. Importantly, the criterion accounts for correlations in both space and time.
T. DelSole, M. K. Tippett
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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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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 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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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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Compatible Transformer for Irregularly Sa: Multivariate Time Series [PDF]
, 2023 To analyze multivariate tune series, most previous methods assume regular subsampling of tune series, where the interval between adjacent measurements and the number of samples remain unchanged.
Peng, J +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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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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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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