Format:
1 Online-Ressource (xvii, 263 pages)
,
illustrations
Edition:
Second edition
ISBN:
9781837635504
,
1837635501
Content:
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.
Note:
Includes index
,
Table of Contents: -- Preface -- Part 1: Getting Started with Prophet -- Chapter 1: The History and Development of Time Series Forecasting -- Chapter 2: Getting Started with Prophet -- Chapter 3: How Prophet Works -- Part 2: Seasonality, Tuning, and Advanced Features -- Chapter 4: Handling Non-Daily Data -- Chapter 5: Working with Seasonality -- Chapter 6: Forecasting Holiday Effects -- Chapter 7: Controlling Growth Modes -- Chapter 8: Influencing Trend Changepoints -- Chapter 9: Including Additional Regressors -- Chapter 10: Accounting for Outliers and Special Events -- Chapter 11: Managing Uncertainty Intervals -- Part 3: Diagnostics and Evaluation -- Chapter 12: Performing Cross-Validation -- Chapter 13: Evaluating Performance Metrics -- Chapter 14: Productionalizing Prophet -- Index.
Additional Edition:
ISBN 1837630410
Additional Edition:
ISBN 9781837630417
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 1837630410
Additional Edition:
ISBN 9781837630417
Language:
English
