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IDENTIFYING MULTIVARIATE TIME SERIES MODELS
Journal of Time Series Analysis, 1989Abstract.This paper is concerned with how canonical variate analysis can be used to identify the structure of a linear multivariate time series model. The procedure used is based on that of Akaike and Cooper and Wood. A correction and a refinement are made, however.
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Multivariate Time Series Decomposition into Oscillation Components
Neural Computation, 2017Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the
Takeru Matsuda, Fumiyasu Komaki
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Perceptual Indexing of Multivariate Time Series
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007We consider the problem of deriving compressed perceptual representation of multivariate time series and using it for efficient indexing and similarity search. Our algorithm is based on the identification of perceptual skeletons in multidimensional space and the use of these "simplifications" in similarity measurements. We illustrate the performance of
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This section examines the development of a multivariate time series function fi, j(t) that encapsulates the impact of one variable on two or more variables. We present the elements that constitute the multidimensional space and demonstrate how multidimensional panel data can facilitate the analysis.
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Classification Based on Compressive Multivariate Time Series
2016Prediction of critical condition in intensive care unit (ICU) becomes one of the current major focuses in hospital healthcare delivery. Most of existing data mining methods only considered single time series signal and worked in original dimension. Consequently, they performed poorly for extended dataset of patient records.
Chandra Utomo +2 more
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A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022Ane Blázquez-García +2 more
exaly
Locally Adaptive Bayesian Multivariate Time Series. [PDF]
, 2013 In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such locally adaptive smoothness is not accounted for, one can obtain misleading inferences and predictions, with ...
DURANTE, DANIELE +2 more
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Statistical Reconstruction of Multivariate Time Series
IEEE Transactions on Signal Processing, 1993openaire +2 more sources