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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.
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Über die Autorin bzw. den Autor
Changquan Huang is an Associate Professor at the Department of Statistics and Data Science, School of Economics, Xiamen University (XMU), China. He obtained his PhD in Statistics from The Chinese University of Hong Kong. For over 18 years, he has taught the course Time Series Analysis at XMU. He has authored and translated monographs in Chinese, including Bayesian Statistics with R (Tsinghua University Press 2017) and Time Series and Financial Data Analysis (China Statistics Press 2004). His research interests now cover applied statistics and artificial intelligence methods for time series.
Alla Petukhina is a Lecturer at the School of Computing, Communication and Business, HTW Berlin, Germany. She was a postdoctoral researcher at the School of Business and Economics at the Humboldt-Universität zu Berlin, where she obtained her PhD in Statistics in 2018. Her research interests include asset allocation strategies, regression shrinkage techniques, quantiles and expectiles, history of statistics and investment strategies with crypto-currencies.
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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.
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Applied Time Series Analysis and Forecasting with Python
ISBN 10: 3031135865
ISBN 13: 9783031135866
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Softcover
Anbieter: moluna, Greven, Deutschland
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Zustand: 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. Bestandsnummer des Verkäufers 1128556070
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Applied Time Series Analysis and Forecasting with Python
Verlag:
Springer International Publishing, 2023
ISBN 10: 3031135865
ISBN 13: 9783031135866
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783031135866
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Applied Time Series Analysis and Forecasting with Python
ISBN 10: 3031135865
ISBN 13: 9783031135866
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. 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 equallyappeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 384 pp. Englisch. Bestandsnummer des Verkäufers 9783031135866
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Applied Time Series Analysis and Forecasting with Python
ISBN 10: 3031135865
ISBN 13: 9783031135866
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: 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 equallyappeal 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. Bestandsnummer des Verkäufers 9783031135866
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Applied Time Series Analysis and Forecasting with Python (Statistics and Computing)
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
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Zustand: New. Bestandsnummer des Verkäufers 18398535762
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Applied Time Series Analysis and Forecasting with Python
Anbieter: Basi6 International, Irving, TX, USA
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Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Bestandsnummer des Verkäufers ABEJUNE24-347957
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Applied Time Series Analysis and Forecasting with Python,
Anbieter: Basi6 International, Irving, TX, USA
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Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Bestandsnummer des Verkäufers ABEJUNE24-18290
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Applied Time Series Analysis and Forecasting with Python
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Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Bestandsnummer des Verkäufers ABNR-45617
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Applied Time Series Analysis and Forecasting with Python
Anbieter: Basi6 International, Irving, TX, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Bestandsnummer des Verkäufers ABEJUNE24-385429
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Applied Time Series Analysis and Forecasting with Python (Statistics and Computing)
Anbieter: Books Puddle, New York, NY, USA
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Zustand: New. Bestandsnummer des Verkäufers 26398535768
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