Forecasting Time Series Data with Prophet - Second Edition: Build, improve, and optimize time series forecasting models using Meta's advanced forecast

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 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.

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 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.

使用Python創建和改進具有強烈季節效應、節假日和其他回歸變量的時間序列數據的完全自動化預測。

購買印刷版或Kindle書籍將包括一本免費的PDF電子書。

主要特點：
- 探索Meta開發的開源預測工具Prophet，以改進您的預測。
- 創建預測並運行診斷以了解預測質量。
- 進行模型微調以實現高性能並使用具體統計數據報告此性能。

書籍描述：
Prophet使Python和R開發人員能夠構建可擴展的時間序列預測。本書將幫助您使用僅需幾行代碼即可實現Prophet的尖端預測技術，以高準確性建模未來數據。

您將首先探索時間序列預測的演變，從基本的早期模型到現代先進模型。在初始安裝和設置之後，您將深入研究Prophet背後的數學和理論。然後，您將涵蓋高級功能，例如可視化預測、添加節假日和趨勢變化點以及處理異常值。您將使用傅立葉級數來建模季節性，了解何時選擇加法模型和乘法模型，以及何時修改每個模型參數。本更新版還新增了有關建模COVID等震蕩的部分。在本書的後半部分，您將了解如何通過超參數調整和添加其他回歸變量來優化更複雜的模型。最後，您將學習如何運行診斷以評估模型的性能，並在生產環境中運行Prophet時發現有用的功能。

通過閱讀本書，您將能夠使用簡潔、易懂且可重複的代碼從原始時間序列數據集構建先進且準確的預測模型。

學到的內容：
- 理解Prophet模型背後的數學原理。
- 使用Python從真實數據集構建實用的預測模型。
- 理解時間序列通常呈現的不同增長模式。
- 發現如何識別和處理時間序列數據中的異常值。
- 了解如何控制不確定區間，以提供預測的百分比置信度。
- 將您的Prophet模型投入生產環境，以更快、更高效地擴展您的工作。

適合閱讀對象：
本書適合企業經理、數據科學家、數據分析師、機器學習工程師和軟件工程師，他們希望在Python或R中構建時間序列預測。為了充分利用本書，您應該對時間序列數據有基本的理解，並能夠將其與其他類型的數據區分開來。對預測技術的基本知識是一個加分項。