Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool
Idioma: inglés
Editorial: Packt Publishing Limited, 2021
- Tapa blanda
- Usado
Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de AmericaWorld of Books (was SecondSale)
Vendedor de IberLibro desde 20 de diciembre de 2007
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EUR 5,43
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Añadir al carritoDescripción del artículo del vendedor
This book will help you get to grips with the task of time series forecasting using the leading open source forecasting tool available to the public, Facebook Prophet. You will learn how to implement the advanced features of Prophet to build forecast models and understand why and how to modify each of the default parameters to improve results.
N° de ref. del artículo 00101383967
- Título
- Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool
- Autor
- Greg Rafferty
- Editorial
- Packt Publishing Limited
- Año de publicación
- 2021
- Estado
- Fair
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1800568533
- ISBN 13
- 9781800568532
Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python
Key Features
- Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts
- Build a forecast and run diagnostics to understand forecast quality
- Fine-tune models to achieve high performance, and report that performance with concrete statistics
Book Description
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.
What you will learn
- Gain an understanding of time series forecasting, including its history, development, and uses
- Understand how to install Prophet and its dependencies
- Build practical forecasting models from real datasets using Python
- Understand the Fourier series and learn how it models seasonality
- Decide when to use additive and when to use multiplicative seasonality
- Discover how to identify and deal with outliers in time series data
- Run diagnostics to evaluate and compare the performance of your models
Who this book is for
This 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.
Table of Contents
- The History and Development of Time Series Forecasting
- Getting Started with Facebook Prophet
- Non-Daily Data
- Seasonality
- Holidays
- Growth Modes
- Trend Changepoints
- Additional Regressors
- Outliers and Special Events
- Uncertainty Intervals
- Cross-Validation
- Performance Metrics
- Productionalizing Prophet
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Greg Rafferty is a data scientist in San Francisco, California. With over a decade of experience, he has worked with many of the top firms in tech, including Google, Facebook, and IBM. Greg has been an instructor in business analytics on Coursera and has led face-to-face workshops with industry professionals in data science and analytics. With both an MBA and a degree in engineering, he is able to work across the spectrum of data science and communicate with both technical experts and non-technical consumers of data alike.
“Acerca de” puede pertenecer a otra edición de este título.
World of Books (was SecondSale)
Montgomery, IL, Estados Unidos de America
Vendedor de IberLibro desde 20 de diciembre de 2007
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