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Publicado por Chapman and Hall/CRC (edition 1), 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
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Idioma: Inglés
Publicado por Chapman and Hall/CRC 2022-08-01, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
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Original o primera edición
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Añadir al carritoCondición: New. 2022. 1st Edition. Hardcover. . . . . .
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Añadir al carritoCondición: New. 1st edition NO-PA16APR2015-KAP.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
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Añadir al carritoHardback. 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 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.
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Añadir al carritoCondición: New. Wayne Woodward, Bivin Sadler, Stephen Robertson 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, L.
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Añadir al carritoHardcover. Condición: Brand New. 512 pages. 10.00x7.00x1.38 inches. In Stock.
Idioma: Inglés
Publicado por Taylor and Francis Ltd, GB, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Librería: Rarewaves.com UK, London, Reino Unido
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Añadir al carritoHardback. 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 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.
ISBN 10: 036753794X ISBN 13: 9780367537944
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Idioma: Inglés
Publicado por Chapman And Hall/CRC Aug 2022, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoBuch. 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.
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 512 pages. 10.00x7.00x1.38 inches. In Stock. This item is printed on demand.
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Añadir al carritoBuch. 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 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.
Librería: preigu, Osnabrück, Alemania
EUR 182,95
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Añadir al carritoBuch. Condición: Neu. Time Series for Data Science | Analysis and Forecasting | Wayne A. Woodward (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2022 | Chapman and Hall/CRC | EAN 9780367537944 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.