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Author: Peter J. Brockwell Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3111325032 Category : Mathematics Languages : en Pages : 522
Book Description
This book provides a self-contained account of continuous-parameter time series, starting with second-order models. Integration with respect to orthogonal increment processes, spectral theory and linear prediction are treated in detail. Lévy-driven models are incorporated, extending coverage to allow for infinite variance, a variety of marginal distributions and sample paths having jumps. The necessary theory of Lévy processes and integration of deterministic functions with respect to these processes is developed at length. Special emphasis is given to the analysis of continuous-time ARMA processes.
Author: Peter J. Brockwell Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3111325032 Category : Mathematics Languages : en Pages : 522
Book Description
This book provides a self-contained account of continuous-parameter time series, starting with second-order models. Integration with respect to orthogonal increment processes, spectral theory and linear prediction are treated in detail. Lévy-driven models are incorporated, extending coverage to allow for infinite variance, a variety of marginal distributions and sample paths having jumps. The necessary theory of Lévy processes and integration of deterministic functions with respect to these processes is developed at length. Special emphasis is given to the analysis of continuous-time ARMA processes.
Author: Andreas S. Weigend Publisher: Routledge ISBN: 042997227X Category : Social Science Languages : en Pages : 665
Book Description
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.
Author: Edward James Hannan Publisher: John Wiley & Sons ISBN: 0470317132 Category : Mathematics Languages : en Pages : 552
Book Description
The Wiley Series in Probability and Statistics is a collection of topics of current research interests in both pure and applied statistics and probability developments in the field and classical methods. This series provides essential and invaluable reading for all statisticians, whether in academia, industry, government, or research.
Author: Emanuel Parzen Publisher: ISBN: Category : Time-series analysis Languages : en Pages : 588
Book Description
On consistent estimates of the spectral density of a stationary time series; Analysis of a general system for the detection of amplitude-modulated noise; A central limit theorem for multilinear stochastic processes; Conditions that a stochastic process ber egodic; On consistent estimates of the spectrum of a stationary time series; On choosing an estimate of the spectral density function of a stationary time series; On asymptotically efficient consistent estimates of the spectral density function of a stationary time series; General considerations in the analysis of spectra; Mathematical considerations in the estimation of spectra; Spectral analysis of asymptotically stationary time series; On spectral analysis with missing observations and amplitude modulation; Notes on fourier analysis and spectral windows; Statistical inference on time series by Hilbert space methods; An approach to time series analysis; Regression analysis of continuous parameter time series; A new approach to the synthesis of optimal smoothing and prediction systems; Probability density functionals and reproducing kernel hilbert spaces; Extraction and detection problems and reproducing kernel hilbert spaces; On estimation of a probability density function and mode; On models for the probability of fatigue failure of a structure; An approach to empirical time series analysis.
Author: Robert A. Yaffee Publisher: Academic Press ISBN: 0127678700 Category : Mathematics Languages : en Pages : 556
Book Description
A time series is a set of repeated measurements of the same phenomenon taken sequentially over time. Capturing the data creates a time series "memory" to document correlations or lack, and to help them make decisions based on this data.
Author: Paul S.P. Cowpertwait Publisher: Springer Science & Business Media ISBN: 0387886982 Category : Mathematics Languages : en Pages : 262
Book Description
This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.
Author: James Durbin Publisher: Oxford University Press ISBN: 9780198523543 Category : Business & Economics Languages : en Pages : 280
Book Description
State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.
Author: Peter J. Brockwell
Author: Peter J. Brockwell
Author: Ulf Grenander
Author: Andreas S. Weigend
Author: Peter C. Young
Author: Stanford University. Applied Mathematics and Statistics Laboratory
Author: Edward James Hannan
Author: Emanuel Parzen
Author: Robert A. Yaffee
Author: Paul S.P. Cowpertwait
Author: James Durbin