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Forecasting Time Series Data with Facebook Prophet

You're reading from  Forecasting Time Series Data with Facebook Prophet

Product type Book
Published in Mar 2021
Publisher Packt
ISBN-13 9781800568532
Pages 270 pages
Edition 1st Edition
Languages
Author (1):
Greg Rafferty Greg Rafferty
Profile icon Greg Rafferty

Table of Contents (18) Chapters

Preface 1. Section 1: Getting Started
2. Chapter 1: The History and Development of Time Series Forecasting 3. Chapter 2: Getting Started with Facebook Prophet 4. Section 2: Seasonality, Tuning, and Advanced Features
5. Chapter 3: Non-Daily Data 6. Chapter 4: Seasonality 7. Chapter 5: Holidays 8. Chapter 6: Growth Modes 9. Chapter 7: Trend Changepoints 10. Chapter 8: Additional Regressors 11. Chapter 9: Outliers and Special Events 12. Chapter 10: Uncertainty Intervals 13. Section 3: Diagnostics and Evaluation
14. Chapter 11: Cross-Validation 15. Chapter 12: Performance Metrics 16. Chapter 13: Productionalizing Prophet 17. Other Books You May Enjoy

To get the most out of this book

To run the code examples in this book, you will need Python 3.x installed. All examples in this book were made using Prophet version 0.71 in Jupyter Notebooks. MacOS, Windows, and Linux are all supported. Although all examples in this book will be written in Python, everything is also fully compatible with R and you may use that language if you prefer, although this book will not cover R syntax. Please refer to the official Prophet documentation for R syntax (https://facebook.github.io/prophet/).

Chapter 2, Getting Started with Facebook Prophet will walk you through installing Facebook Prophet, and installing either Anaconda or Miniconda is strongly recommended in order to correctly install all of Prophet's dependencies. It is possible to install Prophet without using Anaconda, but it can be very difficult depending upon the specific configuration of your machine, and this book will assume Anaconda will be used.

In order to follow the examples, you must at least be familiar with the pandas library for data processing and Matplotlib for making plots. In a few cases, the numpy library will be used to simulate random data but following the examples will not require that you know the NumPy syntax. All of these libraries will be installed automatically as Prophet dependencies, if not already installed. All datasets are hosted and can be downloaded from this book's GitHub repo here: https://github.com/PacktPublishing/Forecasting-Time-Series-Data-with-Facebook-Prophet.

Prophet supports parallelization with Dask but, while setting Prophet up to run on a Dask cluster will be covered, installing and using Dask is beyond the scope of this book. Similarly, this book will cover how to build interactive Prophet visualizations in Plotly but putting those together into a Dash dashboard will be left up to the reader to learn elsewhere.

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to copy/pasting of code.

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