Forecasting Time Series Data with Facebook Prophet

Cover
Title page
Copyright and Credits
Contributors
Table of Contents
Preface
Section 1: Getting Started
The History and Development of Time Series Forecasting
	Understanding time series forecasting
		The problem with dependent data
	Moving average and exponential smoothing
	ARIMA
	ARCH/GARCH
	Neural networks
	Prophet
	Summary
Chapter 2: Getting Started with Facebook Prophet
	Technical requirements
	Installing Prophet
		Installation on macOS
		Installation on Windows
		Installation on Linux
	Building a simple model in Prophet
	Interpreting the forecast DataFrame
	Understanding components plots
	Summary
Section 2: Seasonality, Tuning, and Advanced Features
Chapter 3: Non-Daily Data
	Technical requirements
	Using monthly data
	Using sub-daily data
	Using data with regular gaps
	Summary
Chapter 4: Seasonality
	Technical requirements
	Understanding additive versus multiplicative seasonality
	Controlling seasonality with Fourier order
	Adding custom seasonalities
	Adding conditional seasonalities
	Regularizing seasonality
		Global seasonality regularization
		Local seasonality regularization
	Summary
Chapter 5: Holidays
	Technical requirements
	Adding default country holidays
	Adding default state/province holidays
	Creating custom holidays
	Creating multi-day holidays
	Regularizing holidays
		Global holiday regularization
		Individual holiday regularization
	Summary
Chapter 6: Growth Modes
	Technical requirements
	Applying linear growth
	Understanding the logistic function
	Saturating forecasts
		Increasing logistic growth
		Non-constant cap
		Decreasing logistic growth
	Applying flat growth
	Summary
Chapter 7: Trend Changepoints
	Technical requirements
	Automatic trend changepoint detection
		Default changepoint detection
	Regularizing changepoints
	Specifying custom changepoint locations
	Summary
Chapter 8: Additional Regressors
	Technical requirements
	Adding binary regressors
	Adding continuous regressors
	Interpreting the regressor coefficients
	Summary
Chapter 9: Outliers and Special Events
	Technical requirements
	Correcting outliers that cause seasonality swings
	Correcting outliers that cause wide uncertainty intervals
	Detecting outliers automatically
		Winsorizing
		Standard deviation
		Moving average
		Error standard deviation
	Modeling outliers as special events
	Summary
Chapter 10: Uncertainty Intervals
	Technical requirements
	Modeling uncertainty in trends
	Modeling uncertainty in seasonality
	Summary
Section 3: Diagnostics and Evaluation
Chapter 11: Cross-Validation
	Technical requirements
	Performing k-fold cross-validation
	Performing forward-chaining cross-validation
	Creating the Prophet cross-validation DataFrame
	Parallelizing cross-validation
	Summary
Chapter 12: Performance Metrics
	Technical requirements
	Understanding Prophet's metrics
		Mean squared error
		Root mean squared error
		Mean absolute error
		Mean absolute percent error
		Median absolute percent error
		Coverage
		Choosing the best metric
	Creating the Prophet performance metrics DataFrame
	Handling irregular cut-offs
	Tuning hyperparameters with grid search
	Summary
Chapter 13: Productionalizing Prophet
	Technical requirements
	Saving a model
	Updating a fitted model
	Making interactive plots with Plotly
		Plotly forecast plot
		Plotly components plot
		Plotly single component plot
		Plotly seasonality plot
	Summary
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