Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool (Paperback or Softback)

Rafferty, Greg

ISBN 10: 1800568533 / ISBN 13: 9781800568532

Editore: Packt Publishing 3/12/2021, 2021

Riguardo questo articolo

Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool 1.03.

Codice articolo BBS-9781800568532

Dati bibliografici

• Titolo: Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool (Paperback or Softback)
• Editore: Packt Publishing 3/12/2021
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1800568533
• ISBN 13: 9781800568532
• Rilegatura: Paperback or Softback
• Condizione: New
• Peso: 1,03 libbre
• Tipo di libro: Book

Descrizione articolo

Riassunto

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 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 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.

Informazioni sull?autore

Greg Rafferty is a data scientist in San Francisco, 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 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.