Results 131 to 140 of about 794,789 (182)

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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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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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 Knobbe   +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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Multivariate Time Series Analysis

2007
Nowadays, modern measurement devices are capable to deliver signals with increasing data rates and higher spatial resolutions. When analyzing these data, particular interest is focused on disentangling the network structure underlying the recorded signals.
Björn Schelter, Rainer Dahlhaus, Lutz Leistritz, Wolfram Hesse, Bärbel Schack, Jürgen Kurths, Jens Timmer, Herbert Witte   +7 more
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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 Şişman-Yılmaz, Ferda N. Alpaslan, Lakhmi 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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Multivariate Time Series Decomposition into Oscillation Components

Neural Computation, 2017
Many 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
Matsuda, Takeru, Komaki, Fumiyasu
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Multivariate Time Series

2022
Wayne A. Woodward, Bivin P. Sadler, Stephen D. Robertson   +2 more
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Multivariate Time Series Modeling

1998
Time series models have been applied to many environmental and geohydrological problems. In many instances, such models may be required to provide the most accurate forecasts possible. Before proceeding, a short review of methods will be given.
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