Characterizing Interdependencies Of Multiple Time Series

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an improved version of the basic concepts for measuring the one-way effect, reciprocity, and association of multiple time series, which were originally proposed by Hosoya. Then the statistical inferences of these measures are presented, with a focus on the stationary multivariate autoregressive moving-average processes, which include the estimation and test of causality change. Empirical analyses are provided to illustrate what alternative aspects are detected and how the methods introduced here can be conveniently applied. Most of the materials in Chapters 4 and 5 are based on the authors' latest research work. Subsidiary items are collected in the Appendix.

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.

Rapid technological developments in the last century have brought the field of biomedical engineering into a totally new realm. Breakthroughs in material science, imaging, electronics and more recently the information age have improved our understanding of the human body. As a result, the field of biomedical engineering is thriving with new innovations that aim to improve the quality and cost of medical care. This book is the first in a series of three that will present recent trends in biomedical engineering, with a particular focus on electronic and communication applications. More specifically: wireless monitoring, sensors, medical imaging and the management of medical information.

This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.

Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Expert Systems. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Expert Systems in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

This volume contains the post-proceedings of the Second International Workshop on Critical Information Infrastructure Security (CRITIS 2007), that was held during October 3–5, 2007 in Benalmadena-Costa (Malaga), Spain, and was hosted by the University of Malaga, Computer Science Department. In response to the 2007 call for papers, 75 papers were submitted. Each paper was reviewed by three members of the Program Committee, on the basis of significance, novelty, technical quality and critical infrastructures relevance of the work reported therein. At the end of the reviewing process, only 29 papers were selected for pres- tation. Revisions were not checked and the authors bear full responsibility for the content of their papers. CRITIS 2007 was very fortunate to have four exceptional invited speakers: Adrian Gheorghe (Old Dominion University, USA), Paulo Veríssimo (Universidade de L- boa, Portugal), Donald Dudenhoeffer (Idaho National Labs, USA), and Jacques Bus (European Commission, INFSO Unit "Security"). The four provided a high added value to the quality of the conference with very significant talks on different and int- esting aspects of Critical Information Infrastructures. In 2007, CRITIS demonstrated its outstanding quality in this research area by - cluding ITCIP, which definitively reinforced the workshop. Additionally, the solid involvement of the IEEE community on CIP was a key factor for the success of the event. Moreover, CRITIS received sponsorship from Telecom Italia, JRC of the European Commission, IRRIIS, IFIP, and IABG, to whom we are greatly indebted.

This graduate level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simultaneous equations models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection or specification are treated and a range of tests and criteria for evaluating the adequacy of a chosen model are introduced. The choice of point and interval forecasts is considered and impulse response analysis, dynamic multipliers as well as innovation accounting are presented as tools for structural analysis within the multiple time series context. This book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on this book. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their task. It enables the reader to perform his or her analyses in a gap to the difficult technical literature on the topic.

Three aims of the time series analysis can be distinguished of a finite sample Y(t), t = 1,2, ..., T of a univariate or multivariate time series: (1) Spectral analysis, (2) Model identification, and (3) Prediction. In this paper we consider the case in which a joint autoaggressive scheme is a multiple time series which is stationary, normal, and zero mean. We describe an approach to the solution of these problems of time series analysis through a criterion called CAT (an abbreviation for criterion autoregressive transfer-function). CAT enables one to choose the order of an approximating autoregressive scheme which is 'optimal' in the sense that its transfer function is a minimum overall mean square error estimator (called ARTFACT) of the infinite autoregressive transfer function ARTF) of the filter which transforms the time series to its innovations (white noise). Algorithms for choosing the order of an ARTFACT (autoregressive transfer function approximation converging to the truth) enables one to carry out the approach to empirical multiple time series analysis introduced in Parzen (1969), in particular autoregressive spectral estimation of the spectral density matrix of a stationary multiple time series. Such estimators for univariate time series have been very successfully applied in geophysics (see Ulrych and Bishop (1975)) where they are called 'maximum entropy spectral estimators.' This paper provides a basis for an extension of these procedures to multiple time series.