Time Series Analysis and Its Applications 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 to be useful as a text 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, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. The third edition includes a new section on testing for unit roots and the material on state-space modeling, ARMAX models, and regression with autocorrelated errors have been expanded. Also new to this edition is the enhanced use of the freeware statistical package R. In particular, R code is now included in the text for nearly all of the numerical examples. Data sets and additional R scripts are now provided in one file that may be downloaded via the World Wide Web. This R supplement is a small compressed file that can be loaded easily into R making all the data sets and scripts available to the user with one simple command. The website for the text includes the code used in each example so that the reader may simply copy-and-paste code directly into R. Appendix R, which is new to this edition, provides a reference for the data sets and our R scripts that are used throughout the text. In addition, Appendix R includes a tutorial on basic R commands as well as an R time series tutorial.
This book presents an accessible approach to understanding time series models and their applications. The ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment.
This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures. Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series.
يتناول هذا المؤلف من جديد ـ بشكل أكثر دقة وتصميماً ـ مادة مُدرَّسة بجامعة بيار وماري كوري على مستوى البكالريوس، وهو يفترض معرفة العناصر الأساسية من الطوبولوجيا العامة والتكامل الحسابي والتفاضلي. يتعرض الجزء الأول من الكتاب (الفصول 1-7) إلى جوانب (مجردة) من التحليل الدالي، أما الجزء الثاني من المادة (الفصول 8-10) فيتعلق بدراسة فضاءات دالية (ملموسة) مستعملة في نظرية المعادلات التفاضلية الجزئية، تبين كيف يمكن لمبرهنات وجود(مجردة) أن تسهم في حل معادلات تفاضلية جزئية. هناك ارتباط وثيق بين هذين الفرعين من التحليل: تاريخياً، تطور التحليل الدالي(المجرد) ليجيب عن أسئلة أثيرت عند حل المعادلات التفاضلية الجزئية، وفي المقابل أدى تطور التحليل الدالي (المجرد) إلى تحفيز كبير لنظرية المعادلات التفاضلية الجزئية. سيكون هذا الكتاب مفيداً لكل من الطلبة المهتمين بالرياضيات البحثية، وكذا أولئك المهتمين بالتوجه نحو الرياضيات التطبيقية. العبيكان للنشر
A Guide to the Project Management Body of Knowledge (PMBOK Guide) Fifth Edition reflects the collaboration and knowledge of working project managers and provides the fundamentals of project management as they apply to a wide range of projects. This internationally recognized standard gives project managers the essential tools to practice project management and deliver organizational results. A 10th Knowledge Area has been added; Project Stakeholder Management expands upon the importance of appropriately engaging project stakeholders in key decisions and activities. Project data information and information flow have been redefined to bring greater consistency and be more aligned with the Data, Information, Knowledge and Wisdom (DIKW) model used in the field of Knowledge Management. Four new planning processes have been added: Plan Scope Management, Plan Schedule Management, Plan Cost Management and Plan Stakeholder Management: These were created to reinforce the concept that eac
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.