Results 221 to 230 of about 211,599 (259)

Clustering and Visualization of Multivariate Time Series

2010
The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS through unsupervised clustering and visualization.
Vellido Alcacena, Alfredo, Olier Caparroso, Iván   +1 more
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IDENTIFYING MULTIVARIATE TIME SERIES MODELS

Journal of Time Series Analysis, 1989
Abstract.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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ANFISunfoldedintime for multivariate time series forecasting

Neurocomputing, 2004
This paper proposes a temporal neuro-fuzzy system named ANFIS_unfolded_in_time which is designed to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data by using fuzzy rules. It is a modification of ANFIS neuro-fuzzy model.
N. Arzu Sisman-Yilmaz, Ferda Nur Alpaslan, Lakhmi C. Jain   +2 more
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Multivariate Time Series

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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Robust Exponential Smoothing of Multivariate Time Series

SSRN Electronic Journal, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christophe Croux, Sarah Gelper, Koen Mahieu   +2 more
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Perceptual Indexing of Multivariate Time Series

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
We 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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Classification Based on Compressive Multivariate Time Series

2016
Prediction 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, Xue Li 0001, Sen Wang 0001   +2 more
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Time-delayed Multivariate Time Series Predictions

2023
Hao Niu 0001, Guillaume Habault, Roberto Legaspi, Chuizheng Meng, Defu Cao, Shinya Wada, Chihiro Ono, Yan Liu 0002   +7 more
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Multivariate Time Series

2022
Wayne A. Woodward, Bivin P. Sadler, Stephen D. Robertson   +2 more
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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, Scarpa, Bruno, Dunson, David B.   +2 more
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