Results 261 to 270 of about 234,460 (307)

Biclustering Multivariate Time Series

2017
Sensor networks are able to generate large amounts of unsupervised multivariate time series data. Understanding this data is a non-trivial task: not only patterns in the time series for individual variables can be of interest, it can also be important to understand the relations between patterns in different variables. In this paper, we present a novel
Ricardo Cachucho, Siegfried Nijssen, Arno J. Knobbe   +2 more
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Visual Features for Multivariate Time Series

Proceedings of the 11th International Conference on Advances in Information Technology, 2020
Visual analytics combines the capabilities of computers and humans to explore the insight of data. It provides coupling interactive visual representations with underlying analytical processes (e.g., visual feature extraction) so that users can utilize their cognitive and reasoning capabilities to perform complex tasks effectively or to make decisions ...
Bao Dien Quoc Nguyen, Rattikorn Hewett, Tommy Dang   +2 more
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Early classification on multivariate time series

Neurocomputing, 2015
Multivariate time series (MTS) classification is an important topic in time series data mining, and has attracted great interest in recent years. However, early classification on MTS data largely remains a challenging problem. To address this problem without sacrificing the classification performance, we focus on discovering hidden knowledge from the ...
Guoliang He, Yong Duan, Rong Peng, Xiaoyuan Jing, Tieyun Qian, Lingling Wang   +5 more
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Tennis Multivariate Time Series Clustering

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021
In tennis there are two basic shots (forehand and backhand), which are two of key elements to win points. Sophisticated equipments, such as motion capture systems, enable one to record both the tennis player's movements and tennis racket. The 3D data may be used to define the perfect shot model or to give the directions to the player how to reach to ...
Maria Skublewska-Paszkowska, Pawel Karczmarek, Edyta Lukasik   +2 more
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ESTIMATION OF MULTIVARIATE TIME SERIES

Journal of Time Series Analysis, 1987
Abstract.The algorithm proposed here is a multivariate generalization of a procedure discussed by Pearlman (1980) for calculating the exact likelihood of a univariate ARMA model. Ansley and Kohn (1983) have shown how the Kalman filter can be used to calculate the exact likelihood function when not all the observations are known.
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Stacking for multivariate time series classification

Pattern Analysis and Applications, 2013
This work presents a novel approach to multivariate time series classification. The method exploits the multivariate structure of the time series and the possibilities of the stacking ensemble method. The basics of the method may be described in three steps: first, decomposing the multivariate time series on its constituent univariate time series ...
Oscar J. Prieto, Carlos J. Alonso-González, Juan José Rodríguez Diez   +2 more
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Multivariate Time Series

1987
Many time series arising in practice are best considered as components of some vector-valued (multivariate) time series {X t } whose specification includes not only the serial dependence of each component series {X tj } but also the interdependence between different component series {X ti } and {X tj }.
Peter J. Brockwell, Richard A. Davis
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Multivariate Time Series

2017
In this chapter vector time series models are considered for stationary processes. There is a brief discussion of stationarity, but we leave the reader to refer for further detail to Patterson (2010) and (2011). The models are decomposed into VAR, VMA and mixed models with both characteristics (VARMA).
John Hunter, Simon P. Burke, Alessandra Canepa   +2 more
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Multivariate Time Series Models

1987
The staple of econometrics textbooks, the simultaneous equations model, is a multivariate model; and when the data are time series it becomes a multivariate time series model. John Geweke (1978) laid out the connection of the notation and standard assumptions of simultaneous equations modelling to the corresponding concepts in the theory of vector ...
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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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