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Key benefits

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

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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Time Series Analysis with Python Cookbook

NZ$75.99

Modern Time Series Forecasting with Python

NZ$77.99

Machine Learning with PyTorch and Scikit-Learn

NZ$80.99

Total NZ$ 234.97

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Machiel Kruger Feb 22, 2024

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Archana Dec 13, 2022

This book explores everything from basics of time series to how it can be implemented in using univariate, multivariate and deeplearning algorithms along with the transformations required for implementation.It mainly helps us in the thought process of forecasting and how it can be done efficiently .thanks a lot for the simplistic and elegant explanation.must read for time series enthusiasts.

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Kumar A. Dec 02, 2022

It covers all the aspects of modern time series forecasting. The worked examples are easy to understand and explains the concepts well.This book will help practitioners who will be working with real world time series forecasting problems. Candidates will also find it useful for interview preparations in this field.

Antra Tripathi Dec 21, 2022

This book is a one-stop for holistic learning on time series. Very well-articulated in a simple yet engaging manner. For data enthusiasts who are beginning their career in machine learning, this book is an absolute read as it covers all the essential and key aspects to build up your expertise on time series ground up. For seasoned professionals, it provides a guide to further incorporate deep learning strategies to make your time series modelling better.Key aspects of the book:1.) I really like how the book is been compartmentalized into four parts, beginning from a simple introduction to time series to using deep learning architecture for the same.2.) The fact that the content for each sub-sections/ sub-headings in the chapters are supported with mathematical representations, graphs, flow diagrams, tables, pictures, and code snippets makes it engaging and interesting to follow through.3.) Each chapter has an exhaustive list of references and a bonus reading section which lists research papers, journals, and articles from curated repositories like ACM, MIT, Stanford, IEEE, etc.4.) The book also covers modelling strategy using the open-source library 'Py Torch Forecasting ' in a self-containing and self-explanatory way, supported with detailed explanations and documented code snippets. So even if you are new to using Py Torch, it won't be difficult to follow the content of the book.As a data science professional, who recently finished a master's in machine learning, I found this book really useful for both professional and academic purposes. I really enjoyed reading specialized deep learning architecture for forecasting such as Neural Basis Expansion for Interpretable TS Forecasting (N-BEATSx) and Temporal Fusion Transformers (TFT) followed by Mechanics of multistep forecasting and validation strategies. I definitely see myself implementing these learnings in my work.KUDOS to the author Manu for such excellent encapsulation of the topic with his domain expertise! Happy Forecasting!

Faris Jul 06, 2023

Well written, explainations are detailed enough to get you where you need to go.

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

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