Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool, 2nd Edition - Couverture souple

Greg Rafferty

Couverture souple

ISBN 10 : 1837630410 ISBN 13 : 9781837630417

Editeur : Packt Publishing, 2023

Synopsis

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 Prophet, the open-source forecasting tool developed at Facebook, 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 dependenciesBuild 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 business managers, data scientists, data analysts, machine learning engineers, or software engineers who want to build time-series forecasts in Python or R

Table of Contents

1. History and development of time-series forecasting
2. Getting Started with Prophet
3. How Prophet works
4. Non-Daily data
5. Seasonality
6. Holidays
7. Growth Modes
8. Trend changepoints
9. Additional regressors
10. Outliers and accounting for special events
11. Uncertainty intervals
12. Cross validation
13. Performance metrics
14. Productionalizing Prophet

A propos du livre

À propos de l'auteur

Greg Rafferty is a data scientist at Google in San Francisco, California. With over a decade of experience, he has worked with many of the top firms in tech, including Facebook (Meta) and IBM. Greg has been an instructor in business analytics on Coursera and has led face-to-face workshops with industry professionals in data science and analytics. With both an MBA and a degree in engineering, he is able to work across the spectrum of data science and communicate with both technical experts and non-technical consumers of data alike.

À propos de la quatrième de couverture

Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python Purchase of the print or Kindle book includes a free PDF eBook Key Features:Explore Prophet, the open source forecasting tool developed at Meta, to improve your forecasts Create a forecast and run diagnostics to understand forecast quality Fine-tune models to achieve high performance and report this performance with concrete statistics Book Description: Forecasting Time Series Data with Prophet will help you to implement Prophet's cutting-edge forecasting techniques to model future data with high accuracy using only a few lines of code. This second edition has been fully revised with every update to the Prophet package since the first edition was published two years ago. An entirely new chapter is also included, diving into the mathematical equations behind Prophet's models. Additionally, the book contains new sections on forecasting during shocks such as COVID, creating custom trend modes from scratch, and a discussion of recent developments in the open-source forecasting community. You'll cover advanced features such as visualizing forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. Later, you'll see 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 in production. By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code. What You Will Learn:Understand the mathematics behind Prophet's models Build practical forecasting models from real datasets using Python Understand the different modes of growth that time series often exhibit Discover how to identify and deal with outliers in time series data Find out how to control uncertainty intervals to provide percent confidence in your forecasts Productionalize your Prophet models to scale your work faster and more efficiently Who this book is for: This book is for business managers, data scientists, data analysts, machine learning engineers, and software engineers who want to build time-series forecasts in Python or R. To get the most out of this book, you should have a basic understanding of time series data and be able to differentiate it from other types of data. Basic knowledge of forecasting techniques is a plus.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 1837630410
• ISBN 13: 9781837630417
• Reliure: Broché
• Numéro d'édition: 2
• Nombre de pages: 301
• Coordonnées du fabricant: non disponible
• Personne responsable: GPSR Kontakt; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Allemagne