Forecasting Time Series Data with Facebook Prophet: Build, improve, and optimize time series forecasting models using the advanced forecasting tool

Rafferty, Greg

  • 出版商: Packt Publishing
  • 出版日期: 2021-03-12
  • 定價: $1,650
  • 售價: 9.0$1,485
  • 語言: 英文
  • 頁數: 270
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800568533
  • ISBN-13: 9781800568532
  • 立即出貨 (庫存=1)

相關主題

商品描述

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 fi rst 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.

商品描述(中文翻譯)

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高質量自動化時間序列數據預測


主要特點


  • 學習如何使用開源預測工具Facebook Prophet來改進預測

  • 構建預測並運行診斷以了解預測質量

  • 微調模型以實現高性能,並使用具體統計數據報告該性能


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Prophet的尖端預測技術以更高的準確性和非常少的代碼行來建模未來數據。


您將首先探索時間序列預測的演變,從基本的早期模型到當今的高級模型。本書將演示如何在計算機上安裝和設置Prophet,並僅使用幾行代碼構建您的第一個模型。然後,您將涵蓋高級功能,例如可視化預測、添加節假日、季節性和趨勢變化點、處理異常值等,以及了解修改每個默認參數的原因和方法。後面的章節將向您展示如何通過超參數調整和添加其他回歸變量到模型中來優化更複雜的模型。最後,您將學習如何運行診斷來評估模型的性能,並在生產環境中運行Prophet時看到一些有用的功能。


通過閱讀本Prophet書籍,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。


您將學到什麼


  • 瞭解時間序列預測,包括其歷史、發展和用途

  • 瞭解如何安裝Prophet及其依賴項

  • 使用Python從真實數據集構建實用的預測模型

  • 瞭解傅立葉級數並學習如何建模季節性

  • 決定何時使用加法季節性和何時使用乘法季節性

  • 發現如何識別和處理時間序列數據中的異常值

  • 運行診斷以評估和比較模型的性能


適合閱讀對象

本書適合數據科學家、數據分析師、機器學習工程師、軟件工程師、項目經理和業務經理,他們希望在Python中構建時間序列預測。具備Python的工作知識和基本的預測原則和實踐的理解將有助於更輕鬆地應用本書中介紹的概念。