Forecasting time series data with Facebook Prophet : build, improve, and optimize time series forecasting models using the advanced forecasting tool / Greg Rafferty.

Format:

Book

Author/Creator:

Rafferty, Greg, author.

Language:

English

Subjects (All):

Python (Computer program language).

Time-series analysis.

Online social networks.

R (Computer program language).

Facebook (Electronic resource).

Physical Description:

1 online resource (xii, 265 pages) : illustrations

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2021.

Summary:

Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using PythonKey FeaturesLearn how to use the open-source forecasting tool Facebook Prophet to improve your forecastsBuild a forecast and run diagnostics to understand forecast qualityFine-tune models to achieve high performance, and report that performance with concrete statisticsBook DescriptionProphet 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.What you will learnGain an understanding of time series forecasting, including its history, development, and usesUnderstand how to install Prophet and its dependenciesBuild practical forecasting models from real datasets using PythonUnderstand the Fourier series and learn how it models seasonalityDecide when to use additive and when to use multiplicative seasonalityDiscover how to identify and deal with outliers in time series dataRun diagnostics to evaluate and compare the performance of your modelsWho this book is forThis 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.

Contents:

Cover

Title page

Copyright and Credits

Contributors

Table of Contents

Preface

Section 1: Getting Started

Chapter 1: 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

Section 2: Seasonality, Tuning, and Advanced Features

Chapter 3: Non-Daily Data

Using monthly data

Using sub-daily data

Using data with regular gaps

Chapter 4: Seasonality

Understanding additive versus multiplicative seasonality

Controlling seasonality with Fourier order

Adding custom seasonalities

Adding conditional seasonalities

Regularizing seasonality

Global seasonality regularization

Local seasonality regularization

Chapter 5: Holidays

Adding default country holidays

Adding default state/province holidays

Creating custom holidays

Creating multi-day holidays

Regularizing holidays

Global holiday regularization

Individual holiday regularization

Chapter 6: Growth Modes

Applying linear growth

Understanding the logistic function

Saturating forecasts

Increasing logistic growth

Non-constant cap

Decreasing logistic growth

Applying flat growth

Chapter 7: Trend Changepoints

Automatic trend changepoint detection.

Default changepoint detection

Regularizing changepoints

Specifying custom changepoint locations

Chapter 8: Additional Regressors

Adding binary regressors

Adding continuous regressors

Interpreting the regressor coefficients

Chapter 9: Outliers and Special Events

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

Chapter 10: Uncertainty Intervals

Modeling uncertainty in trends

Modeling uncertainty in seasonality

Section 3: Diagnostics and Evaluation

Chapter 11: Cross-Validation

Performing k-fold cross-validation

Performing forward-chaining cross-validation

Creating the Prophet cross-validation DataFrame

Parallelizing cross-validation

Chapter 12: Performance Metrics

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

Chapter 13: Productionalizing Prophet

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

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Index.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781800566521

1800566522

OCLC:

1240585807