9780367543891 - time series for data science: analysis and forecasting di woodward, wayne a.; sadler, bivin philip; robertson, stephen (19 risultati)
Time Series for Data Science : Analysis and Forecasting
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 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
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Time Series for Data Science : Analysis and Forecasting
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Time Series for Data Science : Analysis and Forecasting
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Lingua: Inglese
Editore: Taylor & Francis Ltd, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Woodward, Wayne A.; Sadler, Bivin Philip; Robertson, Stephen
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: 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
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: New. pages cm.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Paperback. Condizione: 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…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.
Lingua: Inglese
Editore: CRC Press, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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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)
Lingua: Inglese
Editore: Chapman and Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 506 pages. 7.01x1.19x10.00 inches. In Stock.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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Paperback. Condizione: 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…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.
Lingua: Inglese
Editore: CRC Press, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
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Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
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Lingua: Inglese
Editore: Chapman And Hall/CRC Mai 2024, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
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Taschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Taylor & Francis Ltd, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
- Print on Demand
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Lingua: Inglese
Editore: Chapman And Hall/CRC, 2024
Serie: Libro 96 di 59 - Chapman & Hall/CRC Texts in Statistical Science
- Brossura
- Print on Demand
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: 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 mode…lling 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.
