Forecasting Time Series Data with Facebook Prophet

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 first 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.

Table of Contents
The History and Development of Time Series Forecasting
Getting Started with Facebook Prophet
Non-Daily Data
Seasonality
Holidays
Growth Modes
Trend Changepoints
Additional Regressors
Outliers and Special Events
Uncertainty Intervals
Cross-Validation
Performance Metrics
Productionalizing Prophet