Book description
Build realworld time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts
Key Features
 Explore industrytested 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 realworld 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 datadriven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industrytested 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 stateoftheart forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touchedupon topics such as global forecasting models, crossvalidation 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 handson journey in which you’ll develop stateoftheart ML (linear regression to gradientboosted trees) and DL (feedforward neural networks, LSTMs, and transformers) models on a realworld dataset along with exploring practical topics such as interpretability.
By the end of this book, you’ll be able to build worldclass 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 stateoftheart DL models such as NBEATS and Autoformer
 Explore multistep forecasting and crossvalidation strategies
Who this book is for
The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industryready 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
 Modern Time Series Forecasting with Python
 Contributors
 About the author
 About the reviewers
 Preface
 Part 1 – Getting Familiar with Time Series
 Chapter 1: Introducing Time Series
 Chapter 2: Acquiring and Processing Time Series Data
 Chapter 3: Analyzing and Visualizing Time Series Data
 Chapter 4: Setting a Strong Baseline Forecast
 Part 2 – Machine Learning for Time Series
 Chapter 5: Time Series Forecasting as Regression
 Chapter 6: Feature Engineering for Time Series Forecasting

Chapter 7: Target Transformations for Time Series Forecasting
 Technical requirements
 Handling nonstationarity in time series
 Detecting and correcting for unit roots
 Detecting and correcting for trends
 Detecting and correcting for seasonality
 Detecting and correcting for heteroscedasticity
 AutoML approach to target transformation
 Summary
 References
 Further reading
 Chapter 8: Forecasting Time Series with Machine Learning Models
 Chapter 9: Ensembling and Stacking
 Chapter 10: Global Forecasting Models
 Part 3 – Deep Learning for Time Series
 Chapter 11: Introduction to Deep Learning
 Chapter 12: Building Blocks of Deep Learning for Time Series
 Chapter 13: Common Modeling Patterns for Time Series
 Chapter 14: Attention and Transformers for Time Series
 Chapter 15: Strategies for Global Deep Learning Forecasting Models

Chapter 16: Specialized Deep Learning Architectures for Forecasting
 Technical requirements
 The need for specialized architectures
 Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (NBEATS)
 Neural Basis Expansion Analysis for Interpretable Time Series Forecasting with Exogenous Variables (NBEATSx)
 Neural Hierarchical Interpolation for Time Series Forecasting (NHiTS)
 Informer
 Autoformer
 Temporal Fusion Transformer (TFT)
 Interpretability
 Probabilistic forecasting
 Summary
 References
 Further reading
 Part 4 – Mechanics of Forecasting
 Chapter 17: MultiStep Forecasting
 Chapter 18: Evaluating Forecasts – Forecast Metrics
 Chapter 19: Evaluating Forecasts – Validation Strategies
 Index
 Other Books You May Enjoy
Product information
 Title: Modern Time Series Forecasting with Python
 Author(s):
 Release date: November 2022
 Publisher(s): Packt Publishing
 ISBN: 9781803246802
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