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Prévision des données de séries chronologiques avec Fac Prophet : construire, améliorer et optimiser t...-
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- Achat immédiatPrévision des données de séries chronologiques avec Fac Prophet : construire, améliorer et optimiser t...
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Lieu où se trouve l'objet : Jessup, Maryland, États-Unis
Délai de livraison :
Estimé entre le sam. 17 août et le mer. 28 août à 84606
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Numéro de l'objet eBay :384647268934
Dernière mise à jour le 25 juil. 2024 15:06:29 Paris. Afficher toutes les modificationsAfficher toutes les modifications
Caractéristiques de l'objet
- État
- Book Title
- Forecasting Time Series Data with Facebook Prophet: Build, improv
- 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
- 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
Publication Year
2021
Subject
Engineering (General), Machine Theory, Neural Networks, Data Processing
Number of Pages
270 Pages
Language
English
Publication Name
Forecasting Time Series Data with Facebook Prophet : Build, Improve, and Optimize Time Series Forecasting Models Using the Advanced Forecasting Tool
Type
Textbook
Subject Area
Computers, Technology & Engineering
Format
Trade Paperback
Additional Product Features
Dewey Edition
23
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
Description de l'objet fournie par le vendeur
Informations sur le vendeur professionnel
Expert Trading Limited
John Boyer
9220 Rumsey Rd
Ste 101
21045-1956 Columbia, MD
United States
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Numéro de l'objet eBay :384647268934
Dernière mise à jour le 25 juil. 2024 15:06:29 Paris. Afficher toutes les modificationsAfficher toutes les modifications
Livraison et expédition
Lieu où se trouve l'objet :
Jessup, Maryland, États-Unis
Destination :
Afghanistan, Albanie, Algérie, Allemagne, Amérique, Andorre, Angola, Anguilla, Antigua-et-Barbuda, Arabie saoudite, Argentine, Arménie, Aruba, Australie, Autriche, Azerbaïdjan, Bahamas, Bahreïn, Bangladesh, Belgique, Belize, Bermudes, Bhoutan, Bolivie, Bosnie-Herzégovine, Botswana, Brunei Darussalam, Bulgarie, Burkina Faso, Burundi, Bénin, Cambodge, Cameroun, Canada, Cap-Vert, Chili, Chine, Chypre, Colombie, Corée du Sud, Costa Rica, Croatie, Côte d'Ivoire, Danemark, Djibouti, Espagne, Estonie, Europe, Fidji, Finlande, France, Gabon, Gambie, Ghana, Gibraltar, Grenade, Groenland, Grèce, Guatemala, Guinée, Guinée équatoriale, Guinée-Bissau, Guyana, Géorgie, Haïti, Honduras, Hong Kong, Hongrie, Inde, Indonésie, Irlande, Islande, Israël, Italie, Jamaïque, Japon, Jordanie, Kazakhstan, Kenya, Kirghizistan, Kiribati, Koweït, Laos, Lesotho, Lettonie, Liban, Liberia, Liechtenstein, Lituanie, Luxembourg, Macao, Macédoine, Madagascar, Malaisie, Malawi, Maldives, Mali, Malte, Maroc, Maurice, Mauritanie, Mexique, Moldavie, Monaco, Mongolie, Montserrat, Monténégro, Mozambique, Namibie, Nauru, Nicaragua, Niger, Nigeria, Norvège, Nouvelle-Zélande, Népal, Oman, Ouganda, Ouzbékistan, Pakistan, Panama, Papouasie-Nouvelle-Guinée, Paraguay, Pays-Bas, Philippines, Pologne, Portugal, Pérou, Qatar, Roumanie, Royaume-Uni, Rwanda, République centrafricaine, République dominicaine, République du Congo, République démocratique du Congo, République tchèque, Saint-Christophe-et-Niévès, Saint-Marin, Saint-Vincent-et-les-Grenadines, Sainte-Lucie, Salvador, Samoa occidentales, Serbie, Seychelles, Sierra Leone, Singapour, Slovaque, Slovénie, Sri Lanka, Suisse, Suriname, Suède, Swaziland, Sénégal, Tadjikistan, Tanzanie, Taïwan, Tchad, Thaïlande, Togo, Tonga, Trinité-et-Tobago, Tunisie, Turkménistan, Turquie, Uruguay, Vanuatu, Vatican, Viêt Nam, Wallis-et-Futuna, Yémen, Zambie, Zimbabwe, Égypte, Émirats arabes unis, Équateur, Érythrée, Éthiopie, Îles Caïmans, Îles Salomon, Îles Turques-et-Caïques
Exclus :
APO/FPO, Barbade, Guadeloupe, Guyane, Libye, Martinique, Nouvelle-Calédonie, Polynésie française, Protectorats américains, Russie, Réunion, Ukraine, Venezuela
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Received two incorrect books. Weeks passed, seller stopped responding and I had to get ebay to intervene to be able to return them. Waiting on a refund now.
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