Advanced forecasting with python /Korstanje with state-of-the-art-models including LSTms facebooks prophet and amazons deepAR

By:

Korstanje Joos

Language: English Publication details: Apress, c2021. New York :Description: xvii, 296p. : ill. 25 cmISBN:

9781484283400

Subject(s):

DDC classification:

005.133P KOR

Summary: Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting.

Item type: English Books

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Current library	Call number	Status	Date due	Barcode
Anna Centenary Library	005.133P KOR;1 (Browse shelf(Opens below))	Available		679050
Anna Centenary Library 3RD FLOOR, A WING	005.133P KOR (Browse shelf(Opens below))	Checked out	26.11.2024	679049

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Previous	005.133P HEN;1 Learn Data Analysis With Python: Lessons in Coding	005.133P JOH Make Your Own Python Text Adventure: A Guide to Learning Programming	005.133P JOY C and Python Applications: Embedding Python Code in C Programs, SQL Methods, and Python Sockets	005.133P KOR Advanced forecasting with python with state-of-the-art-models including LSTms facebooks prophet and amazons deepAR	005.133P KUM A Journey to core Python experience the applications of tuples, dictionary, lists, operators, loops, indexing, slicing, and matrices	005.133P KUM; 1 A Journey to core Python experience the applications of tuples, dictionary, lists, operators, loops, indexing, slicing, and matrices	005.133P LEN;1 Python continuous integration and delivery a concise guide with examples	Next

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Includes index

Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting.

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Advanced forecasting with python /Korstanje with state-of-the-art-models including LSTms facebooks prophet and amazons deepAR

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