Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool, 2nd Edition

Greg Rafferty

ISBN 10: 1837630410 / ISBN 13: 9781837630417

Editore: Packt Publishing, 2023

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The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Codice articolo GOR014231423

Dati bibliografici

• Titolo: Forecasting Time Series Data with Prophet: Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool, 2nd Edition
• Editore: Packt Publishing
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1837630410
• ISBN 13: 9781837630417
• Rilegatura: Paperback
• Edizione: seconda edizione
• Condizione: Very Good
• Peso: 499 grammi

Descrizione articolo

Riassunto

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:

Forecasting Time Series Data with Prophet 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. This second edition has been fully revised with every update to the Prophet package since the first edition was published two years ago. An entirely new chapter is also included, diving into the mathematical equations behind Prophet's models. Additionally, the book contains new sections on forecasting during shocks such as COVID, creating custom trend modes from scratch, and a discussion of recent developments in the open-source forecasting community.

You'll cover advanced features such as visualizing 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. 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 in production.

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.

Informazioni sull?autore

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.