Book Image

Forecasting Time Series Data with Prophet - Second Edition

By : Greg Rafferty
5 (1)
Book Image

Forecasting Time Series Data with Prophet - Second Edition

5 (1)
By: Greg Rafferty

Overview of this book

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.

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Table of Contents (20 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share your thoughts
Download a free PDF copy of this book
1
Part 1: Getting Started with Prophet
Part 1: Getting Started with Prophet
Free Chapter
2
Chapter 1: The History and Development of Time Series Forecasting
Chapter 1: The History and Development of Time Series Forecasting
Understanding time series forecasting
Moving averages and exponential smoothing
ARIMA
ARCH/GARCH
Neural networks
Prophet
Recent developments
Summary
3
Chapter 2: Getting Started with Prophet
Chapter 2: Getting Started with Prophet
Technical requirements
Installing Prophet
Building a simple model in Prophet
Interpreting the forecast DataFrame
Understanding components plots
Summary
4
Chapter 3: How Prophet Works
Chapter 3: How Prophet Works
Technical requirements
Facebook’s motivation for building Prophet
Analyst-in-the-loop forecasting
The math behind Prophet
Summary
5
Part 2: Seasonality, Tuning, and Advanced Features
Part 2: Seasonality, Tuning, and Advanced Features
6
Chapter 4: Handling Non-Daily Data
Chapter 4: Handling Non-Daily Data
Technical requirements
Using monthly data
Using sub-daily data
Using data with regular gaps
Summary
7
Chapter 5: Working with Seasonality
Chapter 5: Working with Seasonality
Technical requirements
Understanding additive versus multiplicative seasonality
Controlling seasonality with the Fourier order
Adding custom seasonalities
Adding conditional seasonalities
Regularizing seasonality
Summary
8
Chapter 6: Forecasting Holiday Effects
Chapter 6: Forecasting Holiday Effects
Technical requirements
Adding default country holidays
Adding default state/province holidays
Creating custom holidays
Creating multi-day holidays
Regularizing holidays
Summary
9
Chapter 7: Controlling Growth Modes
Chapter 7: Controlling Growth Modes
Technical requirements
Applying linear growth
Understanding the logistic function
Saturating forecasts
Applying flat growth
Creating a custom trend
Summary
10
Chapter 8: Influencing Trend Changepoints
Chapter 8: Influencing Trend Changepoints
Technical requirements
Automatic trend changepoint detection
Regularizing changepoints
Specifying custom changepoint locations
Summary
11
Chapter 9: Including Additional Regressors
Chapter 9: Including Additional Regressors
Technical requirements
Adding binary regressors
Adding continuous regressors
Interpreting the regressor coefficients
Summary
12
Chapter 10: Accounting for Outliers and Special Events
Chapter 10: Accounting for Outliers and Special Events
Technical requirements
Correcting outliers that cause seasonality swings
Correcting outliers that cause wide uncertainty intervals
Detecting outliers automatically
Modeling outliers as special events
Modeling shocks such as COVID-19 lockdowns
Summary
13
Chapter 11: Managing Uncertainty Intervals
Chapter 11: Managing Uncertainty Intervals
Technical requirements
Modeling uncertainty in trends
Modeling uncertainty in seasonality
Summary
14
Part 3: Diagnostics and Evaluation
Part 3: Diagnostics and Evaluation
15
Chapter 12: Performing Cross-Validation
Chapter 12: Performing Cross-Validation
Technical requirements
Performing k-fold cross-validation
Performing forward-chaining cross-validation
Creating the Prophet cross-validation DataFrame
Parallelizing cross-validation
Summary
16
Chapter 13: Evaluating Performance Metrics
Chapter 13: Evaluating Performance Metrics
Technical requirements
Understanding Prophet’s metrics
Creating a Prophet performance metrics DataFrame
Handling irregular cut-offs
Tuning hyperparameters with grid search
Summary
17
Chapter 14: Productionalizing Prophet
Chapter 14: Productionalizing Prophet
Technical requirements
Saving a model
Updating a fitted model
Making interactive plots with Plotly
Summary
18
Index
Index
Why subscribe?
19
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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 1.1 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 Prophet, will walk you through installing 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 assumes that 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 used in this book are hosted and can be downloaded from this book’s GitHub repository here: https://github.com/PacktPublishing/Forecasting-Time-Series-Data-with-Prophet-Second-Edition.

Software/hardware covered in the book

OS requirements

Prophet

Windows, macOS, or Linux (any)

Python 3.7+

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. If interested, I encourage you to refer to the Dask documentation at https://docs.dask.org/en/stable/. 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. The Dash documentation at https://dash.plotly.com/ is a great place to start.

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 copying/pasting code.

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