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ISBN 10: 3031135865 ISBN 13: 9783031135866
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ISBN 10: 3031135865 ISBN 13: 9783031135866
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ISBN 10: 3031135865 ISBN 13: 9783031135866
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ISBN 10: 3031135865 ISBN 13: 9783031135866
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ISBN 10: 3031135865 ISBN 13: 9783031135866
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems. 384 pp. Englisch.
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ISBN 10: 3031135865 ISBN 13: 9783031135866
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ISBN 10: 3031135865 ISBN 13: 9783031135866
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Published by Springer International Publishing, 2023
ISBN 10: 3031135865 ISBN 13: 9783031135866
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equallyappeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
Published by Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031135865 ISBN 13: 9783031135866
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SA.
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ISBN 10: 3031135830 ISBN 13: 9783031135835
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ISBN 10: 3031135830 ISBN 13: 9783031135835
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ISBN 10: 3031135830 ISBN 13: 9783031135835
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ISBN 10: 3031135830 ISBN 13: 9783031135835
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Published by Springer, 2022
ISBN 10: 3031135830 ISBN 13: 9783031135835
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Published by Springer International Publishing Okt 2022, 2022
ISBN 10: 3031135830 ISBN 13: 9783031135835
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems. 384 pp. Englisch.
Published by Springer, 2022
ISBN 10: 3031135830 ISBN 13: 9783031135835
Seller: GreatBookPricesUK, Castle Donington, DERBY, United Kingdom
Condition: New.
Published by Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3031135830 ISBN 13: 9783031135835
Seller: moluna, Greven, Germany
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SA.
Published by Springer International Publishing, 2022
ISBN 10: 3031135830 ISBN 13: 9783031135835
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equallyappeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
Published by Springer Nature, 2022
ISBN 10: 3031135830 ISBN 13: 9783031135835
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 382 pages. 9.25x6.10x0.98 inches. In Stock.
Published by Springer, 2022
ISBN 10: 3031135830 ISBN 13: 9783031135835
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