Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts

Key Features:

• Explore industry-tested machine learning techniques used to forecast millions of time series
• Get started with the revolutionary paradigm of global forecasting models
• Get to grips with new concepts by applying them to real-world datasets of energy forecasting

Book Description:

We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.

This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You'll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you'll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.

By the end of this book, you'll be able to build world-class time series forecasting systems and tackle problems in the real world.

What You Will Learn:

• Find out how to manipulate and visualize time series data like a pro
• Set strong baselines with popular models such as ARIMA
• Discover how time series forecasting can be cast as regression
• Engineer features for machine learning models for forecasting
• Explore the exciting world of ensembling and stacking models
• Get to grips with the global forecasting paradigm
• Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer
• Explore multi-step forecasting and cross-validation strategies

Who this book is for:

The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

建立能夠應用現代機器學習和深度學習概念，擴展到數百萬個時間序列的實際時間序列預測系統。

主要特點：
- 探索用於預測數百萬個時間序列的經過行業驗證的機器學習技術
- 開始使用全球預測模型的革命性範式
- 通過將其應用於能源預測的實際數據集，掌握新概念

書籍描述：
我們生活在一個偶然的時代，數據量的爆炸和對機器學習等數據驅動技術的重新關注改變了分析的風景，以及時間序列預測。這本書充滿了經過行業驗證的技巧和訣竅，將您帶入機器學習（ML）世界的最新技術，超越了常用的傳統統計方法，如ARIMA。

本書是一本全面指南，涵蓋分析、可視化和創建最先進的預測系統的常見主題，包括ML和深度學習（DL），以及很少涉及的主題，如全球預測模型、交叉驗證策略和預測指標。您將從探索數據處理、數據可視化和傳統統計方法的基礎開始，然後轉向時間序列預測的ML和DL模型。本書將帶您進行實踐之旅，開發最先進的ML（從線性回歸到梯度提升樹）和DL（前饋神經網絡、LSTM和Transformer）模型，並探索可解釋性等實用主題。

通過閱讀本書，您將能夠建立世界一流的時間序列預測系統，解決現實世界中的問題。

您將學到什麼：
- 學習如何像專業人士一樣操作和可視化時間序列數據
- 使用流行模型（如ARIMA）建立強大的基準線
- 發現如何將時間序列預測視為回歸問題
- 為機器學習模型的預測工程特徵
- 探索集成和堆疊模型的激動人心世界
- 掌握全球預測範式
- 理解並應用最先進的DL模型，如N-BEATS和Autoformer
- 探索多步預測和交叉驗證策略

本書適合數據科學家、數據分析師、機器學習工程師和Python開發人員，他們希望構建可應用於行業的時間序列模型。由於本書從基礎開始解釋了大多數概念，您只需要基本的Python能力。對機器學習或預測的先前理解將有助於加快學習速度。對於有經驗的機器學習和預測從業人員來說，本書在高級技術和遍歷時間序列預測的最新研究前沿方面提供了很多內容。