Modern time series analysis (44 Ergebnisse)

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Paperback. Zustand: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: 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,450grams, ISBN:9780521565882.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

gebundene Ausgabe. Zustand: Gut. 274 Seiten; Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber. Es befindet sich neben dem Rückenschild lediglich ein Bibliotheksstempel im Buch; ordnungsgemäß entwidmet. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 560.…

gebundene Ausgabe. Zustand: Gut. 274 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten wissenschaftlichen Bibliothek und trägt die entsprechenden Kennzeichnungen (Rückenschild, Instituts-Stempel.); Schnitt und Einband sind etwas staubschmutzig; einige Anstreichungen im Text; der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. Text in ENGLISCHER Sprache! Sprache: Englisch Gewicht in Gramm: 560.…

Zustand: New. In English.

Paperback. Zustand: Very Good. Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Apply ML and global models to improve forecasting accuracy through practical examples Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions Book DescriptionPredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether youre working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, youll learn preprocessing, feature engineering, and model evaluation. As you progress, youll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills. *Email sign-up and proof of purchase requiredWhat you will learn Build machine learning models for regression-based time series forecasting Apply powerful feature engineering techniques to enhance prediction accuracy Tackle common challenges like non-stationarity and seasonality Combine multiple forecasts using ensembling and stacking for superior results Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series Evaluate and validate your forecasts using best practices and statistical metrics Who this book is forThis book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.…

Zustand: New. In English.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. The treatment offers a thorough review of developments in econometric analysis of seasonal time series. Series: Themes in Modern Econometrics. Num Pages: 252 pages, 15 b/w illus. 2 tables. BIC Classification: KCH. Category: (P) Professional & Vocational. Dimension: 229 x 153 x 15. Weight in Grams: 352. . 2010. Illustrated. paperback. . . . . Books ship from the US and Ireland. …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.…

Paperback. Zustand: Brand New. 2nd edition. 332 pages. 9.00x6.25x0.75 inches. In Stock.

Zustand: New. In English.

Zustand: Used. pp. 416.

Taschenbuch. Zustand: Neu. Introduction to Modern Time Series Analysis | Gebhard Kirchgässner (u. a.) | Taschenbuch | Springer Texts in Business and Economics | xii | Englisch | 2014 | Springer | EAN 9783642440298 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New. In English.

Zustand: New. In English.

Gebunden. Zustand: New.

Zustand: New.

Hardcover. Zustand: Brand New. 2nd edition. 331 pages. 9.50x6.50x1.00 inches. In Stock.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 332 | Sprache: Englisch | Produktart: Bücher | This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.…

Zustand: New. In English.

Taschenbuch. Zustand: Neu. The Foundations of Modern Time Series Analysis | Terence C. Mills | Taschenbuch | Palgrave Advanced Texts in Econometrics | xiv | Englisch | 2011 | Palgrave Macmillan | EAN 9781349331352 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New. This book develops the analysis of Time Series from its formal beginnings in the 1890s through to the publication of Box and Jenkins' watershed publication in 1970, showing how these methods laid the foundations for the modern techniques of Time Series analysis that are in use today. Series: Palgrave Advanced Texts in Econometrics. Num Pages: 475 pages, biography. BIC Classification: KCA; KCH; KCZ; PBT. Category: (G) General (US: Trade). Dimension: 235 x 155. . . 2011. Paperback. . . . . Books ship from the US and Ireland. …