Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures

Key Features:

- Apply ML and global models to improve forecasting accuracy through practical examples

- Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS

- Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions

- Purchase of the print or Kindle book includes a free eBook in PDF format

Book Description:

Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you're working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.

Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you'll learn preprocessing, feature engineering, and model evaluation. As you progress, you'll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.

This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.

What You Will Learn:

- Build machine learning models for regression-based time series forecasting

- Apply powerful feature engineering techniques to enhance prediction accuracy

- Tackle common challenges like non-stationarity and seasonality

- Combine multiple forecasts using ensembling and stacking for superior results

- Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series

- Evaluate and validate your forecasts using best practices and statistical metrics

Who this book is for:

This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.

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

(N.B. Please use the Read Sample option to see further chapters)

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

1. Introducing Time Series
2. Acquiring and Processing Time Series Data
3. Analyzing and Visualizing Time Series Data
4. Setting a Strong Baseline Forecast
5. Time Series Forecasting as Regression
6. Feature Engineering for Time Series Forecasting
7. Target Transformations for Time Series Forecasting
8. Forecasting Time Series with Machine Learning Models
9. Ensembling and Stacking
10. Global Forecasting Models
11. Introduction to Deep Learning
12. Building Blocks of Deep Learning for Time Series
13. Common Modeling Patterns for Time Series
14. Attention and Transformers for Time Series
15. Strategies for Global Deep Learning Forecasting Models

(N.B. Please use the Look Inside option to see further chapters)

Ayyan Mani will not be constrained by Indian traditions. Despite working at the Institute of Theory and Research in Mumbai as the lowly personal assistant to a brilliant but insufferable astronomer, he dreams of more for himself and his family.

Ever wily and ambitious, Ayyan weaves two plots: the first to cheer up his weary, soap-opera-addicted wife by creating outrageous fictions around their ten-year-old son; the other to sabotage the married director by using his boss's seeming romance with the institute's first female--and very attractive--researcher. Meanwhile, as the institute's Brahmins wage a vicious war over theories about alien life, Ayyan sees his deceptions intertwining and setting in motion a series of extraordinary events he cannot stop. Unfailingly funny and irreverent, Serious Men is at once a hilarious portrayal of runaway egos and ambitions and a moving portrait of love and its strange workings.

One of 2010's First Novels to Savor. --Sunday Telegraph

The PEN Open Book Award called Manu Joseph that rare bird who can wildly entertain his readers as forcefully as he moves them. In The Illicit Happiness of Other People, Joseph brilliantly brings his talents to the story of an Indian Christian family living far afield in south India.

It has been three years since seventeen-year-old Unni Chacko mysteriously fell from a balcony to his death. His family--journalist father Ousep, who smokes two cigarettes at once because three is too much; mother Mariamma, who fantasizes gleefully about murdering her husband; and twelve-year-old love-struck brother Thoma with zero self-esteem, have coped by not coping. When the post office delivers a comic drawn by Unni that had been lost in the mail, Ousep, shocked out of his stupor, ventures on a quest to understand his son and rewrite his family's story.

Combining family drama with philosophy, social satire with satisfying storytelling, The Illicit Happiness of Other People reminds us that the greatest mystery of all--the one most worth our time and energy--is understanding the people we love.

Bold, stylish and deceptively witty, this gripping new novel by Manu Joseph poses searching questions about political forces and religious division--with an ingenious twist. On the day that Hindu nationalists and their controversial leader have won a spectacular election victory, a large apartment building collapses in Mumbai. The rescue operation finds a single survivor trapped under a beam. The only person able to reach him is Akhila Iyer, a medical student who is also a notorious prankster. Crawling through the rubble to administer first aid, she finds him mumbling in delirium that a young Muslim couple are on their way to carry out a terror attack. Elsewhere, an intelligence agent, Mukundan, is assigned to shadow two terror suspects, one of whom is the teenager Laila, sweetheart of her street. Time is running out and the chase is on... Pervasive in its comic realism, wicked in its humour and broad-based in its canvas, Miss Laila, Armed and Dangerous is a fierce, profound and illuminating portrait of our age of globalisation and populist politics.