Ruben Prien is still at work with the Project, still dreaming of altering man's fate by going back in time to adjust events... to interfere, some might say, with destiny. Once again, his conduit to that bygone era, his messenger to that lost world, is Simon Morley, the man who actually proved himself capable of traveling back and forth in time. Rube's purpose in summoning Si back from that earlier world, where he has taken up permanent residence, is no less grand than an attempt to prevent World War I from erupting. It is ironic, therefore, that the man assigned to carry to America the papers that might help avert the Great Catastrophe travels to his meeting on board the Titanic. And it is Si's task to attempt to ensure his safe passage.
Prince of Time is Book Two in the After Cilmeri Series: Two teenagers are catapulted back in time to alter history and save the medieval kingdom of Wales ... David and his man-at-arms, Ieuan, find themselves alone and on the run from a company of English soldiers who've sworn vengeance for the recent death of their king. Meanwhile, Llywelyn lays on his deathbed, wounded by a traitor's arrow. And once again, it is David and Anna, and all that they represent, that holds the key to the survival of Wales.
Mom touched my underdress--a gown made six hundred years before--and her eyes widened as she rubbed the raw silk between thumb and forefinger. She turned and touched Lia's gown. "Where did you get these clothes?" In Cascade, the second book in the River of Time Series, Gabi knows she's left her heart in the fourteenth century and she persuades Lia to help her to return, even though they know doing so will risk their very lives. When they arrive, weeks have passed and all of Siena longs to celebrate the heroines who turned the tide in the battle against Florence--while the Florentines will go to great lengths to see them dead. But Marcello patiently awaits, and Gabi must decide if she's willing to leave her family behind for good in order to give her heart to him forever.
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 www.crcpress.com. A free online appendix on time series analysis using R can be accessed at http://people.bath.ac.uk/mascc/TSA.usingR.doc. 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.
Release on 1996-08-09 | by Genshiro Kitagawa,Will Gersch
Author: Genshiro Kitagawa,Will Gersch
Pubpsher: Springer Science & Business Media
Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.
Release on 2012-03-05 | by Deepesh Machiwal,Madan Kumar Jha
Theory and Practice
Author: Deepesh Machiwal,Madan Kumar Jha
Pubpsher: Springer Science & Business Media
There is a dearth of relevant books dealing with both theory and application of time series analysis techniques, particularly in the field of water resources engineering. Therefore, many hydrologists and hydrogeologists face difficulties in adopting time series analysis as one of the tools for their research. This book fills this gap by providing a proper blend of theoretical and practical aspects of time sereies analysis. It deals with a comprehensive overview of time series characteristics in hydrology/water resources engineering, various tools and techniques for analyzing time series data, theoretical details of 31 available statistical tests along with detailed procedures for applying them to real-world time series data, theory and methodology of stochastic modelling, and current status of time series analysis in hydrological sciences. In adition, it demonstrates the application of most time series tests through a case study as well as presents a comparative performance evaluation of various time series tests, together with four invited case studies from India and abroad. This book will not only serve as a textbook for the students and teachers in water resources engineering but will also serve as the most comprehensive reference to educate researchers/scientists about the theory and practice of time series analysis in hydrological sciences. This book will be very useful to the students, researchers, teachers and professionals involved in water resources, hydrology, ecology, climate change, earth science, and environmental studies.
The most wanted man in America is about to destroy the entire nation... or save it. It's 2066 and Sandra has kept a low profile for 16 years, working as a tech in a quiet British university, hoping her past would never catch up with her. But it has. When Jay hears Sandra has been kidnapped, he drops everything and goes to the U.S. to find her. But Sandra's kidnapper is not an ordinary criminal. He's America's most-wanted terrorist – a man driven to to free his country from religious oppression at any cost. Sandra, still suffering from the fallout of earlier timesplashes, refuses to help create the biggest timesplash ever, which would unleash a wave of destruction that the rebels hope will kickstart a new American revolution. When Cara, Sandra's teenage daughter, is taken by one of the many factions on the ground in Washington D.C., Sandra's resolve is shaken, and Jay is forced into a race against time to stop the deaths of millions or save Sandra and her daughter. Sandra and Jay must ultimately decide between what is right for them and what is right for all in this thrilling continuation of the Timesplash series. SHORTLISTED FOR BEST SCIENCE FICTION NOVEL AT THE 2014 AUREALIS AWARDS
Mysteries and intrigue from the dramatic era of the French Revolution
Author: Paul Doherty
Pubpsher: Hachette UK
A missing prince may provide the answers - can he be found? The Prince Lost to Time is Paul Doherty's second novel to feature Nicholas Segalla - a shadowy scholar travelling through time solving the past's greatest mysteries. Perfect for fans of Ellis Peters and C. J. Sansom. As the flames of revolution spread through France, they engulf the Royal Family, whose fairy-tale life in the magnificent palace of Versailles is shattered during the violent and bloody Reign of Terror. First to face the executioner is King Louis XVI, followed nine months later by his beautiful queen, the passionate Marie Antoinette. Several months before her death her young son and heir, Louis Charles, is torn from her arms, disappearing into the annals of history for ever. Although many presume him dead, legends spring up about the boy who would be king - did he die? If not, what happened to him? To keep his promise to the doomed queen, Segalla must brave treachery to unlock the answer. What readers are saying about Paul Doherty: 'Wonderful story' 'No one can make you feel as if you're living in different times like Paul Doherty' 'Paul Doherty's books are a joy to read'
This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.