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Prévision des données de séries chronologiques avec Facebook Prophet : construire, améliorer et optimiser,

Sk1drow Books
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24,99 $US
Environ34,52 $C
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Numéro de l'objet eBay :145806892435
Dernière mise à jour : juil. 31, 2024 12:56:06 HAEAfficher toutes les modificationsAfficher toutes les modifications

Caractéristiques de l'objet

État
Bon
Un livre qui a été lu, mais qui est en bon état. La couverture présente des dommages infimes, par exemple des éraflures, mais aucun trou ni aucune déchirure. Dans le cas des livres à reliure, la jaquette peut ne pas être incluse. La reliure présente des traces d'usure minimes. La plupart des pages ne sont pas endommagées et les plis, les déchirures, les passages soulignés ou surlignés et les inscriptions en marge sont minimes. Il n'y a aucune page manquante. Afficher toutes les définitions d'état(s'ouvre dans une nouvelle fenêtre ou un nouvel onglet)
Remarques du vendeur
“Used book in good condition. Shows typical wear. Quick shipping. Satisfaction guaranteed!”
ISBN
9781800568532
Publication Year
2021
Type
Textbook
Format
Trade Paperback
Language
English
Subject Area
Computers, Technology & Engineering
Publication Name
Forecasting Time Series Data with Facebook Prophet : Build, Improve, and Optimize Time Series Forecasting Models Using the Advanced Forecasting Tool
Author
Greg Rafferty
Publisher
Packt Publishing, The Limited
Subject
Engineering (General), Machine Theory, Neural Networks, Data Processing
Number of Pages
270 Pages

À propos de ce produit

Product Identifiers

Publisher
Packt Publishing, The Limited
ISBN-10
1800568533
ISBN-13
9781800568532
eBay Product ID (ePID)
11050393299

Product Key Features

Subject
Engineering (General), Machine Theory, Neural Networks, Data Processing
Publication Year
2021
Number of Pages
270 Pages
Publication Name
Forecasting Time Series Data with Facebook Prophet : Build, Improve, and Optimize Time Series Forecasting Models Using the Advanced Forecasting Tool
Language
English
Type
Textbook
Author
Greg Rafferty
Subject Area
Computers, Technology & Engineering
Format
Trade Paperback

Additional Product Features

Dewey Edition
23
CLASSIFICATION_METADATA
{"IsNonfiction":["Yes"],"IsOther":["No"],"IsAdult":["No"],"MuzeFormatDesc":["Trade Paperback"],"IsChildren":["No"],"Genre":["TECHNOLOGY & ENGINEERING","COMPUTERS"],"Topic":["Data Processing","Engineering (General)","Neural Networks","Machine Theory"],"IsTextBook":["No"],"IsFiction":["No"]}
Dewey Decimal
006.754
Synopsis
Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using PythonKey Features* Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts* Build a forecast and run diagnostics to understand forecast quality* Fine-tune models to achieve high performance, and report that performance with concrete statisticsBook DescriptionProphet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code.You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your fi rst model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments.By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code.What you will learn* Gain an understanding of time series forecasting, including its history, development, and uses* Understand how to install Prophet and its dependencies* Build practical forecasting models from real datasets using Python* Understand the Fourier series and learn how it models seasonality* Decide when to use additive and when to use multiplicative seasonality* Discover how to identify and deal with outliers in time series data* Run diagnostics to evaluate and compare the performance of your modelsWho this book is forThis book is for data scientists, data analysts, machine learning engineers, software engineers, project managers, and business managers who want to build time series forecasts in Python. Working knowledge of Python and a basic understanding of forecasting principles and practices will be useful to apply the concepts covered in this book more easily., Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python Key Features Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts Build a forecast and run diagnostics to understand forecast quality Fine-tune models to achieve high performance, and report that performance with concrete statistics Book Description Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your fi rst model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments. By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code. What You Will Learn Gain an understanding of time series forecasting, including its history, development, and uses Understand how to install Prophet and its dependencies Build practical forecasting models from real datasets using Python Understand the Fourier series and learn how it models seasonality Decide when to use additive and when to use multiplicative seasonality Discover how to identify and deal with outliers in time series data Run diagnostics to evaluate and compare the performance of your models Who this Book is for This book is for data scientists, data analysts, machine learning engineers, software engineers, project managers, and business managers who want to build time series forecasts in Python. Working knowledge of Python and a basic understanding of forecasting principles and practices will be useful to apply the concepts covered in this book more easily.
LC Classification Number
TK5105.88817
ebay_catalog_id
4

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Do not buy this. Does not come with dust jacket and condition is very poor not good as stated. ThriftBooks disappoints once again
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Purchase went well, book arrived in excellent condition
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Thank you! :)
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