Title: Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool, 2nd Edition
Publisher: Packt Publishing
Publication Date: 2023
Language: English
ISBN 10: 1837630410
ISBN 13: 9781837630417
Binding: Soft cover
Edition: 2nd Edition
Condition: New

About this title

Synopsis

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 Prophet, the open-source forecasting tool developed at Facebook, 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 fi rst 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 dependenciesBuild 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 business managers, data scientists, data analysts, machine learning engineers, or software engineers who want to build time-series forecasts in Python or R

History and development of time-series forecasting
Getting Started with Prophet
How Prophet works
Non-Daily data
Seasonality
Holidays
Growth Modes
Trend changepoints
Additional regressors
Outliers and accounting for special events
Uncertainty intervals
Cross validation
Performance metrics
Productionalizing Prophet

About the Author

Greg Rafferty is a data scientist at Google in San Francisco, California. With over a decade of experience, he has worked with many of the top firms in tech, including Facebook (Meta) and IBM. Greg has been an instructor in business analytics on Coursera and has 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 alike.

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