Forecasting Time Series Data with Facebook Prophet
- Paperback: 270 pages
- Publisher: WOW! eBook (March 12, 2021)
- Language: English
- ISBN-10: 1800568533
- ISBN-13: 978-1800568532
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
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.
- 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
By the end of this Forecasting Time Series Data with Facebook 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.