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Forecasting Time Series Data with Facebook Prophet

Forecasting Time Series Data with Facebook Prophet

By : Greg Rafferty
4.9 (17)
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Forecasting Time Series Data with Facebook Prophet

Forecasting Time Series Data with Facebook Prophet

4.9 (17)
By: Greg Rafferty

Overview of this book

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.
Table of Contents (18 chapters)
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1
Section 1: Getting Started
4
Section 2: Seasonality, Tuning, and Advanced Features
13
Section 3: Diagnostics and Evaluation

Making interactive plots with Plotly

In this final section, we'll use the Plotly library to build some interactive plots. Plotly is a completely separate visualization package to the Matplotlib package, which we've been using throughout this book. A plot made with Plotly is richly interactive, allowing tooltips on mouse hover, zooming in and out of a plot, and all sorts of other interactivities.

If you're familiar with Tableau or Power BI, Plotly brings similar interactivity to Python. Additionally, the Plotly team also built Dash, a library for creating web-based dashboards. A full tutorial for creating such a dashboard is beyond the scope of this book, but I encourage you to learn this valuable tool if you would like to share your Prophet forecasts with a wide audience.

Prophet does not automatically install Plotly as a dependency, so before we begin, you will need to install it on your machine. It is a simple process and can be accomplished through either conda...

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Forecasting Time Series Data with Facebook Prophet
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