Forecasting Time Series Data with Prophet
Packt Publishing Limited (Verlag)
978-1-83763-041-7 (ISBN)
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 DescriptionForecasting Time Series Data with Prophet 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. This second edition has been fully revised with every update to the Prophet package since the first edition was published two years ago. An entirely new chapter is also included, diving into the mathematical equations behind Prophet's models. Additionally, the book contains new sections on forecasting during shocks such as COVID, creating custom trend modes from scratch, and a discussion of recent developments in the open-source forecasting community.
You'll cover advanced features such as visualizing 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. Later, 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 in production.
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.What you will learn
Understand the mathematics behind Prophet’s 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 forThis 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.
Greg Rafferty is a data scientist at Google in San Francisco, California. With over a decade of experience, he has worked with many of the top firms in tech, including Facebook (Meta) 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.
Table of Contents
Product Information Document
The History and Development of Time Series Forecasting
Getting Started with Prophet
How Prophet Works
Handling Non-Daily Data
Working with Seasonality
Forecasting Holiday Effects
Controlling Growth Modes
Influencing Trend Changepoints
Including Additional Regressors
Accounting for Outliers and Special Events
Managing Uncertainty Intervals
Performing Cross-Validation
Evaluating Performance Metrics
Productionalizing Prophet
| Erscheinungsdatum | 05.04.2023 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| ISBN-10 | 1-83763-041-0 / 1837630410 |
| ISBN-13 | 978-1-83763-041-7 / 9781837630417 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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