Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
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Published by T&F India, 2025
Language: English
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Hardcover. Condition: New. ISBN:9781032972190,Territorial restriction maybe printed on the book. This is an Int'l edition, ISBN and cover may differ from US edition, Contents same as US edition.
Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pages cm First edition Includes bibliographical references and index.
Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
Seller: Biblios, Frankfurt am main, HESSE, Germany
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Published by Taylor & Francis Ltd, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Published by Chapman and Hall/CRC, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Published by Taylor and Francis Ltd, GB, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Add to basketPaperback. Condition: 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.
Published by Chapman and Hall/CRC (edition 1), 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
Seller: BooksRun, Philadelphia, PA, U.S.A.
Hardcover. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Taylor & Francis Ltd, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
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Add to basketPaperback. Condition: new. Paperback. Data Science students and practitioners want to find a forecast that works and dont 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 doesnt 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. Practical Time Series Analysis for Data Science is an accessible guide that doesnt require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Taylor & Francis Ltd, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Chapman and Hall/CRC 2022-07-07, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
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Published by Taylor and Francis Ltd, GB, 2024
ISBN 10: 0367543893 ISBN 13: 9780367543891
Language: English
Seller: Rarewaves.com UK, London, United Kingdom
£ 75.27
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Add to basketPaperback. Condition: 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.
Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Published by Taylor & Francis Ltd, London, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condition: new. Hardcover. Data Science students and practitioners want to find a forecast that works and dont 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 doesnt 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. Practical Time Series Analysis for Data Science is an accessible guide that doesnt require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Published by Chapman and Hall/CRC, 2022
ISBN 10: 036753794X ISBN 13: 9780367537944
Language: English
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. 1st edition NO-PA16APR2015-KAP.