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Causally appropriate graphical modelling for time series with applications to economics, ecology and environmental science : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Mathematics and Statistics in the University of Canterbury /Meurk, Carla. January 2005 (has links)
Thesis (M. Sc.)--University of Canterbury, 2006. / Typescript (photocopy). Includes bibliographical references (leaves 108-110). Also available via the World Wide Web.
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Estudo da metodologia de analise de series temporais aplicada a monitoracao do tempo de resposta dos instrumentos ligados ao sistema de protecao de reatores nuclearesLOPES, PAULO R. de L. 09 October 2014 (has links)
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02752.pdf: 9313866 bytes, checksum: e570b355f24e91b0efa7ade917c2797b (MD5) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP
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Estudo da metodologia de analise de series temporais aplicada a monitoracao do tempo de resposta dos instrumentos ligados ao sistema de protecao de reatores nuclearesLOPES, PAULO R. de L. 09 October 2014 (has links)
Made available in DSpace on 2014-10-09T12:40:44Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:06:41Z (GMT). No. of bitstreams: 1
02752.pdf: 9313866 bytes, checksum: e570b355f24e91b0efa7ade917c2797b (MD5) / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP
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Price forecasting models in online flower shop implementationLu, Zhen Cang January 2017 (has links)
University of Macau / Faculty of Science and Technology / Department of Computer and Information Science
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Statistical Analysis of Meteorological DataPerez Melo, Sergio 01 January 2014 (has links)
Some of the more significant effects of global warming are manifested in the rise of temperatures and the increased intensity of hurricanes. This study analyzed data on Annual, January and July temperatures in Miami in the period spanning from 1949 to 2011; as well as data on central pressure and radii of maximum winds of hurricanes from 1944 to present.
Annual Average, Maximum and Minimum Temperatures were found to be increasing with time. Also July Average, Maximum and Minimum Temperatures were found to be increasing with time. On the other hand, no significant trend could be detected for January Average, Maximum and Minimum Temperatures.
No significant trend was detected in the central pressures and radii of maximum winds of hurricanes, while the radii of maximum winds for the largest hurricane of the year showed an increasing trend.
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Statistical analysis with the state space modelChu-Chun-Lin, Singfat 05 1900 (has links)
The State Space Model (SSM) encompasses the class of multivariate linear models, in
particular, regression models with fixed, time-varying and random parameters, time series models, unobserved components models and combinations thereof. The well-known
Kalman Filter (KF) provides a unifying tool for conducting statistical inferences with
the SSM.
A major practical problem with the KF concerns its initialization when either the
initial state or the regression parameter (or both) in the SSM are diffuse. In these situations, it is common practice to either apply the KF to a transformation of the data which
is functionally independent of the diffuse parameters or else initialize the KF with an arbitrarily large error covariance matrix. However neither approach is entirely satisfactory.
The data transformation required in the first approach can be computationally tedious
and furthermore it may not preserve the state space structure. The second approach is
theoretically and numerically unsound. Recently however, De Jong (1991) has developed
an extension of the KF, called the Diffuse Kalman Filter (DKF) to handle these diffuse
situations. The DKF does not require any data transformation.
The thesis contributes further to the theoretical and computational aspects of con
ducting statistical inferences using the DKF. First, we demonstrate the appropriate initialization of the DKF for the important class of time-invariant SSM’s. This result is
useful for maximum likelihood statistical inference with the SSM. Second, we derive and
compare alternative pseudo-likelihoods for the diffuse SSM. We uncover some interesting
characteristics of the DKF and the diffuse likelihood with the class of ARMA models.
Third, we propose an efficient implementation of the DKF, labelled the collapsed DKF (CDKF). The latter is derived upon sweeping out some columns of the pertinent matrices
in the DKF after an initial number of iterations. The CDKF coincides with the KF in
the absence of regression effects in the SSM. We demonstrate that in general the CDKF
is superior in practicality and performance to alternative algorithms proposed in the literature. Fourth, we consider maximum likelihood estimation in the SSM using an EM
(Expectation-Maximization) approach. Through a judicious choice of the complete data,
we develop an CDKF-EM algorithm which does not require the evaluation of lag one
state error covariance matrices for the most common estimation exercise required for the
SSM, namely the estimation of the covariance matrices of the disturbances in the SSM.
Last we explore the topic of diagnostic testing in the SSM. We discuss and illustrate the
recursive generation of residuals and the usefulness of the latters in pinpointing likely
outliers and points of structural change. / Business, Sauder School of / Graduate
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A theory of nonlinear systemsBose, Amar G January 1956 (has links)
Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering, 1956. / "June, 1956." / Includes bibliographical references (leaf 113). / Introduction: A physically realizable nonlinear system, like a linear one, is a system whose present output is a function of the past of its input. We may regard the system as a computer that operates on the past of one time function to yield the present value of another time function. Mathematically we say that the system performs a transformation on the past of its input to yield its present output. When this transformation is linear (the case of linear systems) we can take advantage of the familiar convolution integral to obtain the present output from the past of the input and the system is said to be characterized by its response to an impulse. That is, the response of a linear system to an impulse is sufficient to determine its response to any input. When the transformation is nonlinear we no longer have a simple relation like the convolution integral relating the output to the past of the input and the system can no longer be characterized by its response to an impulse since superposition does not apply. Wiener has shown, however, that we can characterize a nonlinear system by a set of coefficients and that these coefficients can be determined from a knowledge of the response of the system to shot noise excitation. Thus, shot noise occupies the same position as a probe for investigating nonlinear systems that the impulse occupies as a probe for investigating linear systems. The first section of this thesis is devoted to the Wiener theory of nonlinear system characterization. Emphasis is placed on important concepts of this theory that are used in succeeding chapters to develop a theory for determining optimum nonlinear systems. / by Amar Gopal Bose. / Sc.D.
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Analysis of expressway time series data and their role in traffic operationsAhmed, Mohamed Samir January 1976 (has links)
No description available.
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Management of chance constrained systems using time series analysis /Hsu, Cheng January 1982 (has links)
No description available.
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Using an intensive time-series design to examine daily achievement and attitude of eighth- and ninth-grade earth science students grouped by cognitive tendency, sex and IQ /Monk, John Stephen January 1983 (has links)
No description available.
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