9781837635504_

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 Prophet, the open-source forecasting tool developed at Facebook, 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 Prophets 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. Youll 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, youll 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 dependenciesBuild 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 business managers, data scientists, data analysts, machine learning engineers, or software engineers who want to build time-series forecasts in Python or R

History and development of time-series forecasting
Getting Started with Prophet
How Prophet works
Non-Daily data
Seasonality
Holidays
Growth Modes
Trend changepoints
Additional regressors
Outliers and accounting for special events
Uncertainty intervals
Cross validation
Performance metrics
Productionalizing Prophet

...(*_*)9781837635504_

Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Explore Prophet, the open source forecasting tool developed at Meta, to improve your forecasts
Create a forecast and run diagnostics to understand forecast quality
Fine-tune models to achieve high performance and report this performance with concrete statistics

Book Description

Prophet empowers Python and R developers to build scalable time series forecasts. This book will help you to implement Prophets cutting-edge forecasting techniques to model future data with high accuracy using only a few lines of code.

Youll begin by exploring the evolution of time series forecasting, from basic early models to present-day advanced models. After the initial installation and setup, youll take a deep dive into the mathematics and theory behind Prophet. Youll then cover advanced features such as visualizing your forecasts, adding holidays and trend changepoints, and handling outliers. Youll 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 youll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, youll 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, youll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.

What you will learn

Understand the mathematics behind Prophets models
Build practical forecasting models from real datasets using Python
Understand the different modes of growth that time series often exhibit
Discover how to identify and deal with outliers in time series data
Find out how to control uncertainty intervals to provide percent confidence in your forecasts
Productionalize your Prophet models to scale your work faster and more efficiently

Who this book is for

This book is for business managers, data scientists, data analysts, machine learning engineers, and software engineers who want to build time-series forecasts in Python or R. To get the most out of this book, you should have a basic understanding of time series data and be able to differentiate it from other types of data. Basic knowledge of forecasting techniques is a plus.

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Key Features

Book Description

What you will learn

Who This Book Is For

Table of Contents

Key Features

Book Description

What you will learn

Who this book is for

Forecasting Time Series Data with Prophet

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