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Key benefits

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

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 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. 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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Time Series Analysis with Python Cookbook

£38.99

Forecasting Time Series Data with Facebook Prophet

£39.99

Machine Learning for Time-Series with Python

£41.99

Total £ 120.97

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Amazon verified reviews

Luis Ek Mar 25, 2021

Phenomenal book. A must read if you are a newbie in forecasting or you already using Facebook prophet in your forecasting suite system. The book starts off with a brief introduction to time series methods that are used across the forecasting domain. It provides the steps, with code, on how to get it running in your machine (OS or Windows), then goes into how to implement, debug it and even how to speed its process using parallelization techniques - (multi threading or dask). It is a perfect addition to the current product documentation.

Amazon Verified review

Amazon Customer Apr 21, 2021

Forecasting Time Series Data with Facebook Prophet is a rather beginner-friendly book with hands-on code examples. It goes over the proper setup of Facebook Prophet in different OS, and how to deploy time-series models on data to yield results for further analysis. One thing I like about the content of this book is its elaboration on the input arguments to each time-series models in Prophet. For example, in the Seasonality chapter, the two options, ''multiplicative" vs. "additive'", for the seasonality_mode are elegantly discussed and moreover, I was able to grasp in an intuitive level when I should use "multiplicative", and when I should use "additive" by comparing the graphs the author provided in the chapter.The difficulty of the topics discussed in the book is structured sequentially from easy to hard, so as I was reading through the chapters without skipping any parts, I didn't encounter many difficulties in comprehending the analysis and technicality.Thus, I strongly recommend this book to time-series analysis beginners to gain some basic understanding of time-series analysis and Prophet implementation, as well as to mid-level readers for a knowledge refresh. One suggestion for the potential readers, if you are reading the electronic version, is to open two editors while reading the book, or put the book on both monitor screens, as some analysis on the graphs on the current page is served as a comparison to the graphs several pages ago or in the previous chapter. You might want to open two of the book so you know how the graph improves from the graph on a different page.

Stacey H R May 03, 2021

This book is clearly written and comprehensive. It provides end-to-end insight on time series analysis and how to best utilize Prophet. Whilst the Prophet documentation provides overviews and sample code, this book goes into much more detail explaining why you might want to use particular components and provides useful example data sets for hands-on experience for a range of scenarios. A great read!

John Bowman Jan 09, 2022

This e-book was well worth the $32 I paid. The writing is very clear with great examples. The e-book formatting is very good as well. I can use this content right away.

A. Robertson Mar 16, 2021

An excellent and clear guide to FB Prophet. Excellent examples and good coding technique makes this a very useable book. Suitable for those with some basic knowledge of Pandas and Python as well as anyone looking to get good forecasts. Makes it much clearer than the product documentation (which is already pretty good).

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

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