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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

Using data with regular gaps

Throughout your career, you may encounter datasets with regular gaps in reporting, particularly when the data was collected by humans who have working hours, personal hours, and sleeping hours. It simply may not be possible to collect measurements with perfect periodicity.

As you will see when we look at outliers in a later chapter, Prophet is robust in handling missing values. However, when that missing data occurs at regular intervals, Prophet will have no training data at all during those gaps to make estimations with. The seasonality will be constrained during periods where data exists but unconstrained during the gaps, and Prophet's predictions can see much larger fluctuations than the actual data displayed. Let's see this in action.

Suppose that Divvy's data had only been collected between the hours of 8am and 6pm each day. We can simulate this by removing data outside these hours from our DataFrame:

df = df[(df['ds&apos...
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Forecasting Time Series Data with Facebook Prophet
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