Non-linear and Non-stationary Time Series Analysis
Author: Maurice Bertram Priestley
Publisher:
Published: 1988
Total Pages: 250
ISBN-13:
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Non-linear and Non-stationary Time Series Analysis
Author: M. B. Priestley
Publisher:
Published: 1991
Total Pages: 237
ISBN-13: 9780125649117
DOWNLOAD EBOOKNonlinear Time Series Analysis
Author: Holger Kantz
Publisher: Cambridge University Press
Published: 2004
Total Pages: 390
ISBN-13: 9780521529020
DOWNLOAD EBOOKThe paradigm of deterministic chaos has influenced thinking in many fields of science. Chaotic systems show rich and surprising mathematical structures. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic. The most direct link between chaos theory and the real world is the analysis of time series from real systems in terms of nonlinear dynamics. Experimental technique and data analysis have seen such dramatic progress that, by now, most fundamental properties of nonlinear dynamical systems have been observed in the laboratory. Great efforts are being made to exploit ideas from chaos theory wherever the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics, and many other sciences.
Nonlinear and Nonstationary Signal Processing
Author: W. J. Fitzgerald
Publisher: Cambridge University Press
Published: 2000
Total Pages: 510
ISBN-13: 9780521800440
DOWNLOAD EBOOKSignal processing, nonlinear data analysis, nonlinear time series, nonstationary processes.
Nonlinear Time Series
Author: Randal Douc
Publisher: CRC Press
Published: 2014-01-06
Total Pages: 548
ISBN-13: 1466502347
DOWNLOAD EBOOKThis text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.
Topics In Nonlinear Time Series Analysis, With Implications For Eeg Analysis
Author: Andreas Galka
Publisher: World Scientific
Published: 2000-02-18
Total Pages: 360
ISBN-13: 9814493929
DOWNLOAD EBOOKThis book provides a thorough review of a class of powerful algorithms for the numerical analysis of complex time series data which were obtained from dynamical systems. These algorithms are based on the concept of state space representations of the underlying dynamics, as introduced by nonlinear dynamics. In particular, current algorithms for state space reconstruction, correlation dimension estimation, testing for determinism and surrogate data testing are presented — algorithms which have been playing a central role in the investigation of deterministic chaos and related phenomena since 1980. Special emphasis is given to the much-disputed issue whether these algorithms can be successfully employed for the analysis of the human electroencephalogram.
Statistics in Volcanology
Author: Heidy M. Mader
Publisher: Geological Society of London
Published: 2006
Total Pages: 304
ISBN-13: 9781862392083
DOWNLOAD EBOOKStatistics in Volcanology is a comprehensive guide to modern statistical methods applied in volcanology written by today's leading authorities. The volume aims to show how the statistical analysis of complex volcanological data sets, including time series, and numerical models of volcanic processes can improve our ability to forecast volcanic eruptions. Specific topics include the use of expert elicitation and Bayesian methods in eruption forecasting, statistical models of temporal and spatial patterns of volcanic activity, analysis of time series in volcano seismology, probabilistic hazard assessment, and assessment of numerical models using robust statistical methods. Also provided are comprehensive overviews of volcanic phenomena, and a full glossary of both volcanological and statistical terms. Statistics in Volcanology is essential reading for advanced undergraduates, graduate students, and research scientists interested in this multidisciplinary field.
Forecasting: principles and practice
Author: Rob J Hyndman
Publisher: OTexts
Published: 2018-05-08
Total Pages: 380
ISBN-13: 0987507117
DOWNLOAD EBOOKForecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Using R for Principles of Econometrics
Author: Constantin Colonescu
Publisher: Lulu.com
Published: 2017-12-28
Total Pages: 278
ISBN-13: 1387473611
DOWNLOAD EBOOKThis is a beginner's guide to applied econometrics using the free statistics software R. It provides and explains R solutions to most of the examples in 'Principles of Econometrics' by Hill, Griffiths, and Lim, fourth edition. 'Using R for Principles of Econometrics' requires no previous knowledge in econometrics or R programming, but elementary notions of statistics are helpful.
Nonlinear Time Series Analysis
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
Published: 2018-09-13
Total Pages: 516
ISBN-13: 1119264065
DOWNLOAD EBOOKA comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.
