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.
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