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Synopsis
Practical Time Series Analysis for Data Science 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.
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Wayne Woodward, Bivin Sadler, Stephen Robertson
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Time Series for Data Science: Analysis and Forecasting
Edité par
Taylor & Francis Ltd, 2024
ISBN 10 : 0367543893
ISBN 13 : 9780367543891
Neuf
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Time Series for Data Science
Edité par
Chapman And Hall/CRC Mai 2024, 2024
ISBN 10 : 0367543893
ISBN 13 : 9780367543891
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9780367543891
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Edité par
Chapman and Hall/CRC, 2024
ISBN 10 : 0367543893
ISBN 13 : 9780367543891
Neuf
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Practical time series analysis for data science
Edité par
Chapman and Hall/CRC, 2024
ISBN 10 : 0367543893
ISBN 13 : 9780367543891
Neuf
Couverture souple
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Time Series for Data Science
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Time Series for Data Science : Analysis and Forecasting
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Chapman and Hall/CRC, 2024
ISBN 10 : 0367543893
ISBN 13 : 9780367543891
Neuf
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Time Series for Data Science
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Time Series for Data Science
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Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)
Edité par
Chapman and Hall/CRC, 2024
ISBN 10 : 0367543893
ISBN 13 : 9780367543891
Neuf
Couverture souple
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Time Series for Data Science : Analysis and Forecasting
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9780367543891
Quantité disponible : 1 disponible(s)