Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models, 2nd Edi tion

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

Prophet empowers 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 high accuracy using only a few lines of code.

You'll begin by exploring the evolution of time series forecasting, from basic early models to present-day advanced models. After the initial installation and setup, you'll take a deep dive into the mathematics and theory behind Prophet. You'll then cover advanced features such as visualizing your 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. This updated edition has a new section on modeling shocks such as COVID. Later on in the book 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 and discover useful features when running Prophet in production environments.

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.
Table of Contents

The History and Development of Time Series Forecasting
Getting Started with Prophet
How Prophet Works
Handling Non-Daily Data
Working with Seasonality
Forecasting Holiday Effects
Controlling Growth Modes
Influencing Trend Changepoints
Including Additional Regressors
Accounting for Outliers and Special Events
Managing Uncertainty Intervals
Performing Cross-Validation
Evaluating Performance Metrics
Productionalizing Prophet