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

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

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 Modelling Patterns for Time Series
Attention and Transformers for Time Series
Strategies for Global Deep Learning Forecasting Models
Specialized Deep Learning Architectures for Forecasting
Multi-Step Forecasting
Evaluating Forecasts – Forecast Metrics
Evaluating Forecasts – Validation Strategies