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

Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python. Prophet empowers Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecas...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rafferty, Greg (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:Second edition.
Temas:
Time-series analysis > Data processing.
Python (Computer program language)
Machine learning.
Série chronologique > Informatique.
Python (Langage de programmation)
Apprentissage automatique.
Machine learning
Time-series analysis > Data processing
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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520 |a Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python. Prophet empowers 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 high accuracy using only a few lines of code. You'll begin by exploring the evolution of time series forecasting, from basic early models to present-day advanced models. After the initial installation and setup, you'll take a deep dive into the mathematics and theory behind Prophet. You'll then cover advanced features such as visualizing your forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. This updated edition has a new section on modeling shocks such as COVID. Later on in the book you'll see 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 discover useful features when running Prophet in production environments. By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code. 
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