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

by Manu Joseph

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts 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. 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. (N.B. Please use the Look Inside option to see further chapters)

552 pages, Paperback

Published November 24, 2022

Reviews

[Anas Zulkifli · Rating 4 out of 5]

Good exposure and implementation of the new modern time series techniques and ML development. However, the book assumes you have prior knowledge of time series forecasting from the Econometrics class at a minimum. It would be nice if the book puts keynotes on each metric or techniques on how to actually read the values (if X metric is high, what does it mean? What would be the acceptable range for Y metric as a good indicator that the predictions are reliable?) rather than just showing how to implement it. The notebooks also require updating as of March 2024 to produce the said outputs.

[Kyle Murphy · Rating 4 out of 5]

Covers nearly everything under the sun with regard to time series forecasting…would like to see an “end to end” example from data->prep->model->deployment->monitoring.

[Josua Naiborhu · Rating 5 out of 5]

amazing book alongside the hands-on implementation and clear wrapper class for constructing the code on each use cases.