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