Xing haipeng (97 resultados)

Paperback. Condición: Very Good. 7. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condición: acceptable. Fast Free Shipping â" A well-loved copy with text fully readable and cover pages intact. May display wear such as writing, highlighting, bends, folds or library marks. Still a complete and usable book.

Paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Condición: very_good. This book is in Very good condition. There may be a few flaws like shelf wear and some light wear.

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9781498795630.

Hardcover. Condición: USED BOOKS. USED BOOKS ! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Condición: New.

Condición: New.

Condición: New. pp. 376.

Paperback. Condición: New. The field of time series analysis has undergone a remarkable transformation since the publication of the seventh edition of this book. While classical statistical models such as autoregressive integrated moving average (ARIMA), state-space models, and spectral methods remain essential, the rise of artificial intelligence (AI) has introduced groundbreaking approaches to modelling, forecasting, and generating time-dependent data. This eighth edition of The Analysis of Time Series: An Introduction with R reflects these advancements with the addition of two new chapters: Predictive AI for Time Series and Generative AI for Time Series. These chapters bridge the gap between traditional time series methods and cutting-edge AI techniques, offering readers a comprehensive and integrated perspective on the field.FeaturesComprehensive coverage of classical time series models including ARIMA, state-space models, and spectral methodsTwo new chapters on predictive and generative AI, introducing cutting-edge methods like transformers, variational autoencoders, and diffusion modelsPractical examples and illustrations using R, demonstrating the application of both classical and AI-based approaches to real-world time series dataEmphasis on the integration of classical statistical rigor with the flexibility and scalability of AI methodsClear explanations and intuitive insights, making advanced concepts accessible to a broad audienceUpdated content reflecting the latest developments in time series analysis, with a focus on modern, high-dimensional, and nonlinear data challengesThe Analysis of Time Series: An Introduction with R, Eighth Edition is designed for students, researchers, and practitioners in statistics, as well as in finance, economics, climate science, health, and engineering. It serves as both a foundational text for those new to time series analysis and a valuable resource for experienced analysts seeking to engage with the rapidly evolving landscape of predictive and generative AI. With its balance of theory, practical implementation, and real-world examples, the book is ideal for use in academic courses, professional training, and self-study.…

Condición: New.

Condición: New. pp. 376 Illus.

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Condición: New.

Condición: New. 2026. 8th Edition. paperback. . . . . .

Paperback. Condición: New. The field of time series analysis has undergone a remarkable transformation since the publication of the seventh edition of this book. While classical statistical models such as autoregressive integrated moving average (ARIMA), state-space models, and spectral methods remain essential, the rise of artificial intelligence (AI) has introduced groundbreaking approaches to modelling, forecasting, and generating time-dependent data. This eighth edition of The Analysis of Time Series: An Introduction with R reflects these advancements with the addition of two new chapters: Predictive AI for Time Series and Generative AI for Time Series. These chapters bridge the gap between traditional time series methods and cutting-edge AI techniques, offering readers a comprehensive and integrated perspective on the field.FeaturesComprehensive coverage of classical time series models including ARIMA, state-space models, and spectral methodsTwo new chapters on predictive and generative AI, introducing cutting-edge methods like transformers, variational autoencoders, and diffusion modelsPractical examples and illustrations using R, demonstrating the application of both classical and AI-based approaches to real-world time series dataEmphasis on the integration of classical statistical rigor with the flexibility and scalability of AI methodsClear explanations and intuitive insights, making advanced concepts accessible to a broad audienceUpdated content reflecting the latest developments in time series analysis, with a focus on modern, high-dimensional, and nonlinear data challengesThe Analysis of Time Series: An Introduction with R, Eighth Edition is designed for students, researchers, and practitioners in statistics, as well as in finance, economics, climate science, health, and engineering. It serves as both a foundational text for those new to time series analysis and a valuable resource for experienced analysts seeking to engage with the rapidly evolving landscape of predictive and generative AI. With its balance of theory, practical implementation, and real-world examples, the book is ideal for use in academic courses, professional training, and self-study.…

Condición: New.

paperback. Condición: Gut. 376 Seiten; 9781441926685.3 Gewicht in Gramm: 1.

Condición: As New. Unread book in perfect condition.

Condición: NEW.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.