Search Results: nonlinear-time-series-analysis-with-r

Nonlinear Time Series Analysis with R

Author: Ray Huffaker,Marco Bittelli,Rodolfo Rosa

Publisher: Oxford University Press

ISBN: 0191085790

Category: Mathematics

Page: 312

View: 2029

Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians—with limited knowledge of nonlinear dynamics—to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic. The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework—condensed from sound empirical practices recommended in the literature—that details a step-by-step procedure for applying NLTS in real-world data diagnostics.

Nonlinear Time Series

Theory, Methods and Applications with R Examples

Author: Randal Douc,Eric Moulines,David Stoffer

Publisher: CRC Press

ISBN: 1466502258

Category: Mathematics

Page: 551

View: 575

Designed for researchers and students, Nonlinear Times Series: Theory, Methods and Applications with R Examples familiarizes readers with the principles behind nonlinear time series models—without overwhelming them with difficult mathematical developments. By focusing on basic principles and theory, the authors give readers the background required to craft their own stochastic models, numerical methods, and software. They will also be able to assess the advantages and disadvantages of different approaches, and thus be able to choose the right methods for their purposes. The first part can be seen as a crash course on "classical" time series, with a special emphasis on linear state space models and detailed coverage of random coefficient autoregressions, both ARCH and GARCH models. The second part introduces Markov chains, discussing stability, the existence of a stationary distribution, ergodicity, limit theorems, and statistical inference. The book concludes with a self-contained account on nonlinear state space and sequential Monte Carlo methods. An elementary introduction to nonlinear state space modeling and sequential Monte Carlo, this section touches on current topics, from the theory of statistical inference to advanced computational methods. The book can be used as a support to an advanced course on these methods, or an introduction to this field before studying more specialized texts. Several chapters highlight recent developments such as explicit rate of convergence of Markov chains and sequential Monte Carlo techniques. And while the chapters are organized in a logical progression, the three parts can be studied independently. Statistics is not a spectator sport, so the book contains more than 200 exercises to challenge readers. These problems strengthen intellectual muscles strained by the introduction of new theory and go on to extend the theory in significant ways. The book helps readers hone their skills in nonlinear time series analysis and their applications.

Time Series Analysis and Its Applications

With R Examples

Author: Robert H. Shumway,David S. Stoffer

Publisher: Springer

ISBN: 3319524526

Category: Mathematics

Page: 562

View: 4050

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.

Statistik II für Dummies

Author: Deborah J. Rumsey

Publisher: John Wiley & Sons

ISBN: 3527669248

Category: Mathematics

Page: 372

View: 1713

Es gibt Qualen, verdammte Qualen und Statistik, so sehen es viele Studenten. Mit ?Statistik II f?r Dummies? lernen Sie so leicht wie m?glich. Deborah Rumsey zeigt Ihnen, wie Sie Varianzanalysen und Chi-Quadrat-Test machen, wie Sie mit Regressionen arbeiten, ein Modell erstellen, Korrelationen bilden und vieles mehr. So lernen Sie die Methoden, die Sie brauchen, und erhalten das Handwerkszeug, erfolgreich Ihre Statistikpr?fungen zu bestehen.

Angewandte Zeitreihenanalyse mit R

Author: Rainer Schlittgen

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 311041399X

Category: Business & Economics

Page: 329

View: 7444

Dieses Buch präsentiert die wichtigsten Modelle und Verfahren der Zeitreihenanalyse. Der Schwerpunkt liegt auf dem Zeitbereich; speziell werden explorative Methoden, ARMA-Modelle mit ihren Erweiterungen, Prognosemethoden und Zeitreihenregressionen behandelt. Die Neuauflage wurde akualisiert und unter anderem um ein Kapitel der Long-Memory-Prozesse erweitert.

Time Series Analysis

With Applications in R

Author: Jonathan D. Cryer,Kung-Sik Chan

Publisher: Springer Science & Business Media

ISBN: 038775959X

Category: Mathematics

Page: 491

View: 514

This book has been developed for a one-semester course usually attended by students in statistics, economics, business, engineering, and quantitative social sciences. A unique feature of this edition is its integration with the R computing environment. Basic applied statistics is assumed through multiple regression. Calculus is assumed only to the extent of minimizing sums of squares but a calculus-based introduction to statistics is necessary for a thorough understanding of some of the theory. Actual time series data drawn from various disciplines are used throughout the book to illustrate the methodology.

Time Series Analysis

Methods and Applications

Author: Tata Subba Rao,Suhasini Subba Rao,Calyampudi Radhakrishna Rao

Publisher: Elsevier

ISBN: 0444538585

Category: Mathematics

Page: 755

View: 6768

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respective areas

Nonlinear Time Series Analysis

Author: Ruey S. Tsay,Rong Chen

Publisher: Wiley

ISBN: 1119264057

Category: Mathematics

Page: 512

View: 2825

A 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.

Nonlinear Time Series Analysis

Author: Holger Kantz,Thomas Schreiber

Publisher: Cambridge University Press

ISBN: 9780521529020

Category: Mathematics

Page: 369

View: 5187

New edition of a successful advanced text on nonlinear time series analysis.

Einführung in die Statistik der Finanzmärkte

Author: Jürgen Franke,Wolfgang Karl Härdle,Christian Matthias Hafner

Publisher: Springer-Verlag

ISBN: 3642170498

Category: Business & Economics

Page: 428

View: 4920

The Analysis of Time Series

An Introduction, Sixth Edition

Author: Chris Chatfield

Publisher: CRC Press

ISBN: 9780203491683

Category: Mathematics

Page: 352

View: 6789

Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets. The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from A free online appendix on time series analysis using R can be accessed at Highlights of the Sixth Edition: A new section on handling real data New discussion on prediction intervals A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series A new chapter of examples and practical advice Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few years The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.

Wahrscheinlichkeitstheorie und Stochastische Prozesse

Author: Michael Mürmann

Publisher: Springer-Verlag

ISBN: 364238160X

Category: Mathematics

Page: 428

View: 7888

Dieses Lehrbuch beschäftigt sich mit den zentralen Gebieten einer maßtheoretisch orientierten Wahrscheinlichkeitstheorie im Umfang einer zweisemestrigen Vorlesung. Nach den Grundlagen werden Grenzwertsätze und schwache Konvergenz behandelt. Es folgt die Darstellung und Betrachtung der stochastischen Abhängigkeit durch die bedingte Erwartung, die mit der Radon-Nikodym-Ableitung realisiert wird. Sie wird angewandt auf die Theorie der stochastischen Prozesse, die nach der allgemeinen Konstruktion aus der Untersuchung von Martingalen und Markov-Prozessen besteht. Neu in einem Lehrbuch über allgemeine Wahrscheinlichkeitstheorie ist eine Einführung in die stochastische Analysis von Semimartingalen auf der Grundlage einer geeigneten Stetigkeitsbedingung mit Anwendungen auf die Theorie der Finanzmärkte. Das Buch enthält zahlreiche Übungen, teilweise mit Lösungen. Neben der Theorie vertiefen Anmerkungen, besonders zu mathematischen Modellen für Phänomene der Realität, das Verständnis.​

Einführung in die moderne Zeitreihenanalyse

Author: Gebhard Kirchgässner,Jürgen Wolters

Publisher: Vahlen

ISBN: 9783800632688

Category: Econometrics

Page: 244

View: 4395

Wahrscheinlichkeitsrechnung und Statistik

Author: Robert Hafner

Publisher: Springer-Verlag

ISBN: 3709169445

Category: Mathematics

Page: 512

View: 3946

Das Buch ist eine Einführung in die Wahrscheinlichkeitsrechnung und mathematische Statistik auf mittlerem mathematischen Niveau. Die Pädagogik der Darstellung unterscheidet sich in wesentlichen Teilen – Einführung der Modelle für unabhängige und abhängige Experimente, Darstellung des Suffizienzbegriffes, Ausführung des Zusammenhanges zwischen Testtheorie und Theorie der Bereichschätzung, allgemeine Diskussion der Modellentwicklung – erheblich von der anderer vergleichbarer Lehrbücher. Die Darstellung ist, soweit auf diesem Niveau möglich, mathematisch exakt, verzichtet aber bewußt und ebenfalls im Gegensatz zu vergleichbaren Texten auf die Erörterung von Meßbarkeitsfragen. Der Leser wird dadurch erheblich entlastet, ohne daß wesentliche Substanz verlorengeht. Das Buch will allen, die an der Anwendung der Statistik auf solider Grundlage interessiert sind, eine Einführung bieten, und richtet sich an Studierende und Dozenten aller Studienrichtungen, für die mathematische Statistik ein Werkzeug ist.

Bilinear Stochastic Models and Related Problems of Nonlinear Time Series Analysis

A Frequency Domain Approach

Author: György Terdik

Publisher: Springer Science & Business Media

ISBN: 9780387988726

Category: Mathematics

Page: 260

View: 7112

"The book should prove valuable to students interested in nonlinear time series analysis and applications, to research workers is nonlinear stochastic analysis, and to people interested in practical data analysis."--BOOK JACKET.

The Econometric Analysis of Seasonal Time Series

Author: Eric Ghysels,Denise R. Osborn

Publisher: Cambridge University Press

ISBN: 9780521565882

Category: Business & Economics

Page: 228

View: 553

The treatment offers a thorough review of developments in econometric analysis of seasonal time series.

Nonlinear Dynamics and Chaos with Applications to Hydrodynamics and Hydrological Modelling

Author: Slavco Velickov

Publisher: CRC Press

ISBN: 9058096912

Category: Science

Page: 336

View: 9644

A hydroinformatics system represents an electronic knowledge encapsulator that models part of the real world and can be used for the simulation and analysis of physical, chemical and biological processes in water systems, in order to achieve a better management of the aquatic environment. Thus, modelling is at the heart of hydroinformatics. The theory of nonlinear dynamics and chaos, and the extent to which recent improvements in the understanding of inherently nonlinear natural processes present challenges to the use of mathematical models in the analysis of water and environmental systems, are elaborated in this work. In particular, it demonstrates that the deterministic chaos present in many nonlinear systems can impose fundamental limitations on our ability to predict behaviour, even when well-defined mathematical models exist. On the other hand, methodologies and tools from the theory of nonlinear dynamics and chaos can provide means for a better accuracy of short-term predictions as demonstrated through the practical applications in this work.

Topics in Nonlinear Time Series Analysis

With Implications for EEG Analysis

Author: Andreas Galka

Publisher: World Scientific

ISBN: 9789810241483

Category: Mathematics

Page: 342

View: 3678

This 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.

Modelle der Zeitreihenanalyse

Author: Manfred Deistler,Wolfgang Scherrer

Publisher: Springer-Verlag

ISBN: 331968664X

Category: Mathematics

Page: 159

View: 2284

Dieses Buch bietet eine einheitliche und geschlossene Darstellung von Theorie und Modellen, die der Zeitreihenanalyse zugrunde liegen. Das Schwergewicht liegt dabei beim schwach stationären Fall und bei linearen Modellen: Im ersten Teil wird die Theorie allgemeiner multivariater schwach stationärer Prozesse in Zeit-und Frequenzbereich, einschließlich deren Prognose und Filterung hergeleitet. Der zweite Teil beschäftigt sich mit multivariaten AR-, ARMA- und Zustandsraum-Systemen als den wichtigsten Modellklassen für stationäre Prozesse. In diesem Rahmen werden Yule-Walker Gleichungen, die Faktorisierung rationaler Spektren, das Kalman Filter und die Struktur von ARMA-und Zustandsraum-Systemen beschrieben. Ziel des Buches ist es die wesentlichen Konzepte, Ideen, Methoden und Resultate in mathematisch sauberer Form darzustellen und somit eine solide Fundierung für Studenten und Forscher in Feldern wie datengetriebener Modellierung, Prognose und Filterung, wie sie etwa für die Kontrolltheorie, Ökonometrie, Signalverarbeitung und Statistik relevant sind, zu bieten.

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