9780367543891 - time series for data science: analysis and forecasting (chapman & hall/crc texts in statistical science) de woodward, wayne a.; sadler, bivin philip; robertson, stephen (19 resultados)
Time Series for Data Science : Analysis and Forecasting
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Time Series for Data Science : Analysis and Forecasting
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Time Series for Data Science : Analysis and Forecasting
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
- Tapa blanda
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Time Series for Data Science : Analysis and Forecasting
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
- Tapa blanda
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
- Tapa blanda
Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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Condición: New. pages cm First edition Includes bibliographical references and index.
Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Editorial: Taylor and Francis Ltd, GB, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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Paperback. Condición: New. Data Science students and practitioners want to find a forecast that "works" and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering t…ime series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.Features:Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.
Idioma: Inglés
Editorial: CRC Press, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Time Series for Data Science: Analysis and Forecasting (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A. (Author)/ Sadler, Bivin Philip (Author)/ Robertson, Stephen (Author)
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Paperback. Condición: Brand New. 506 pages. 7.01x1.19x10.00 inches. In Stock.
Idioma: Inglés
Editorial: Taylor and Francis Ltd, GB, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Paperback. Condición: New. Data Science students and practitioners want to find a forecast that "works" and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering t…ime series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.Features:Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.
Idioma: Inglés
Editorial: CRC Press, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Idioma: Inglés
Editorial: Chapman And Hall/CRC Mai 2024, 2024
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data Science students and practitioners want to find a forecast that 'works' and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble…modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.Features:Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use. 530 pp. Englisch.
Idioma: Inglés
Editorial: Taylor & Francis Ltd, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Idioma: Inglés
Editorial: Chapman And Hall/CRC, 2024
Serie: Libro 96 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data Science students and practitioners want to find a forecast that 'works' and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble model…ling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.Features:Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.
