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Code: USD75OFF
This is the code repository for Forecasting Time Series Data with Prophet, published by Packt.
Forecasting Time Series Data with Prophet
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, handling outliers, and more. You’ll use the Fourier series to model seasonality and learn how to choose between an additive or multiplicative model, and understand when to modify each model parameter. This updated edition has a new section on modeling shocks such as COVID. 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 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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
model = Prophet(seasonality_mode='multiplicative',
yearly_seasonality=4,
n_changepoints=5)
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-14).
| Chapter | Software required | OS required |
|---|---|---|
| 1-14 | Prophet | Windows, Mac OS X, and Linux (Any) |
| 1-14 | Python 3.7+ | Windows, Mac OS X, and Linux (Any) |
Greg Rafferty is a data scientist at Google, in Mountain View, California. With over a decade of experience, he has worked with many of the top firms in tech including Google, Facebook, and IBM. Greg has been an instructor in business analytics on Coursera and led face-to-face workshops with industry professionals in data science and analytics. With both an MBA and a degree in engineering, he is able to work across the spectrum of data science and communicate with both technical experts and non-technical consumers of data with equal ease.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.