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.
Table of Contents
- Introducing Time Series
- Acquiring and Processing Time Series Data
- Analyzing and Visualizing Time Series Data
- Setting a Strong Baseline Forecast
- Time Series Forecasting as Regression
- Feature Engineering for Time Series Forecasting
- Target Transformations for Time Series Forecasting
- Forecasting Time Series with Machine Learning Models
- Ensembling and Stacking
- Global Forecasting Models
- Introduction to Deep Learning
- Building Blocks of Deep Learning for Time Series
- Common Modeling Patterns for Time Series
- Attention and Transformers for Time Series
- Strategies for Global Deep Learning Forecasting Models
(N.B. Please use the Look Inside option to see further chapters)
Joseph, Manu: - Manu Joseph is a self-made data scientist with more than a decade of experience working with many Fortune 500 companies enabling digital and AI transformations, specifically in machine learning-based demand forecasting. He is considered an expert, thought leader, and strong voice in the world of time series forecasting. Currently, Manu leads applied research at Thoucentric, where he advances research by bringing cutting-edge AI technologies to the industry. He is also an active open-source contributor and developed an open-source library-PyTorch Tabular-which makes deep learning for tabular data easy and accessible. Originally from Thiruvananthapuram, India, Manu currently resides in Bengaluru, India, with his wife and son
