Forecasting time series data with Prophet : build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool / by Greg Rafferty

By:

Rafferty, Greg [author]

Material type:

Computer fileLanguage: English Publication details: Birmingham, UK : Packt Publishing Ltd, 2023Edition: 2nd. edDescription: 1 online resource (282, pages) : color illustrationsContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781837635504 (e-book)

Subject(s):

LOC classification:

QA280 R12 2023

Online resources:

Contents:

Part 1. Getting started with prophet -- 1. The history and development of time series forecasting -- 2. Getting started with prophet -- 3. How prophet works -- Part 2. Seasonality, tuning, and advanced features -- 4. Handling non-daily data -- 5. Working with seasonality -- 6. Forecasting holiday effects -- 7. Controlling growth modes -- 8. Influencing trend changepoints -- 9. Including additional regressors -- 10. Accounting for outliers and special events -- 11. Managing uncertainty intervals -- Part 3. Diagnostics and evaluation -- 12. Performing cross-validation -- 13. Evaluating performance metrics -- 14. Productional zing prophet

Summary: 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 fi rst 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.

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Holdings ( 2 )
Title notes ( 5 )

Holdings
Item type	Current library	Collection	Call number	Materials specified	Status	Notes	Date due	Barcode
Online E-Books	Ladislao N. Diwa Memorial Library Multimedia Section	Non-fiction	OEBP QA280 R12 2023 (Browse shelf(Opens below))		Available	PAV		OEBP000454
Compact Discs	Ladislao N. Diwa Memorial Library Multimedia Section	Non-fiction	EB QA280 R12 2023 (Browse shelf(Opens below))		Room use only	PAV		CD0001383

https://portal.igpublish.com/iglibrary/ is required to read this e-book.

Includes index

Part 1. Getting started with prophet -- 1. The history and development of time series forecasting -- 2. Getting started with prophet -- 3. How prophet works -- Part 2. Seasonality, tuning, and advanced features -- 4. Handling non-daily data -- 5. Working with seasonality -- 6. Forecasting holiday effects -- 7. Controlling growth modes -- 8. Influencing trend changepoints -- 9. Including additional regressors -- 10. Accounting for outliers and special events --
11. Managing uncertainty intervals -- Part 3. Diagnostics and evaluation -- 12. Performing cross-validation -- 13. Evaluating performance metrics -- 14. Productional zing prophet

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 fi rst 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.

Fund 164 CE-Logic Purchased February 19, 2024 OEBP000454
P. Roderno PHP 5,586.00
2024-02-0124 2024-1-113