Modern Time Series Forecasting with Python

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

Key Features

• Explore industry-tested machine learning techniques to forecast millions of time series
• Get started with the revolutionary paradigm of global forecasting models
• Learn 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 renewed interest in data-driven techniques like machine learning (ML) has changed the landscape of analytics and with it time series forecasting. This book attempts to take you beyond the commonly used classical statistical methods like ARIMA and introduce to you the latest techniques from the world of ML.

The book is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics like ML and deep learning (DL), and rarely touched upon topics like global forecasting models, cross-validation strategies, and forecast metrics. We start off with the basics of data handling and visualization and classical statistical methods and very soon move on to ML and DL models for time series forecasting.

By the end of the book, which is filled with industry-tested tips and tricks, you will have mastery over time series forecasting and will have acquired enough skills to tackle problems in the real world.

What you will learn

• Learn how to manipulate and visualize time series data like a pro
• Set strong baselines with popular models like 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
• Learn about the global forecasting paradigm
• Understand and apply state-of-the-art deep learning models like N-BEATS, AutoFormer, and more
• Discover multi-step forecasting and cross-validation strategies

Who This Book Is For

The book is ideal 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. A prior understanding of machine learning or forecasting would help speed up the learning. For seasoned practitioners in machine learning and forecasting, the book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

Table of Contents

1. Introducing Time Series
2. Acquiring and Processing Time Series Data
3. Analyzing and Visualizing Time Series Data
4. Setting a Strong Baseline Forecast
5. Time Series Forecasting as Regression
6. Feature Engineering for Time Series Forecasting
7. Target Transformations for Time Series Forecasting
8. Forecasting Time Series with Machine Learning Models
9. Ensembling and Stacking
10. Global Forecasting Models
11. Introduction to Deep Learning
12. Building Blocks of Deep Learning for Time Series
13. Common Modelling Patterns for Time Series
14. Attention and Transformers for Time Series
15. Strategies for Global Deep Learning Forecasting Models
16. Specialized Deep Learning Architectures for Forecasting
17. Multi-Step Forecasting
18. Evaluating Forecasts - Forecast Metrics
19. Evaluating Forecasts - Validation Strategies