Joseph / Tackes Modern Time Series Forecasting with Python

Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
1. Auflage 2024
ISBN: 978-1-83588-319-8
Verlag: Packt Publishing
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 660 Seiten

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.
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Autoren/Hrsg.

Joseph, Manu

Tackes, Jeffrey

Leseproben

Preface

Mankind has always sought the ability to predict the future. Since the earliest civilizations, people have tried to predict the future. Shamans, oracles, and prophets used anything ranging from astrology and palmistry to numerology to satisfy the human need to see into the future. In the last century, with the developments in IT, the mantle of predicting the future landed on data analysts and data scientists. And how do we predict the future? It’s not by examining the lines and creases on our hands or the positions of the stars anymore but by using data that has been generated in the past. And instead of prophecies, we now have forecasts.

Time, being the fourth dimension in our world, makes all the data generated in the world time series data. All the data that is generated in the real world has an element of time associated with it. Whether the temporal aspect is relevant to the problem or not is another question altogether. However, to be more concrete and immediate, we can find time series forecasting use cases in many industries, such as retail, energy, healthcare, and finance. We might want to know how many units of a particular product are to be dispatched to a particular store, or we might want to know how much electricity is to be produced to meet demand.

In this book, using a real-world dataset, you will learn how to handle and visualize time series data using and , generate baseline forecasts using , and use machine learning and deep learning for forecasting, using popular Python libraries such as and . We conclude the book with a few chapters that cover seldom-touched aspects, such as multi-step forecasting, forecast metrics and cross validation for time series.

The book will enable you to build real-world time series forecasting systems that scale to millions of time series by mastering and applying modern concepts in machine learning and deep learning.

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.

What this book covers

Part 1—Getting Familiar with Time Series

, , is all about introducing you to the world of time series. We lay down a definition of time series and talk about how it is related to a Data Generating Process (DGP). We will also talk about the limits of forecasting and talk about what we cannot forecast, and then we finish off the chapter by laying down some terminology that will help you understand the rest of the book.

, , covers how you can process time series data. You will understand how different forms of time series data can be represented in a tabular form. You will learn different date-time-related functionalities in and learn how to fill in missing data using techniques suited for time series. Finally, using a real-world dataset, you will go through a step-by-step journey in processing time series data using .

, , furthers your introduction to time series by learning how to visualize and analyze time series. You will learn different visualizations that are commonly used for time series data and then learn how to go one level deeper by decomposing time series into its components. To wrap it up, you will also look at ways to identify and treat outliers in time series data.

, , gets right to the topic of time series forecasting as we use tried and tested methods from econometrics, such as and , to generate strong baselines. These efficient forecasting methods will provide strong baselines so that we can go beyond these classical techniques and learn modern techniques, such as machine learning. You will also get an introduction to another key topic—assessing forecastability using techniques such as and .

Part 2—Machine Learning for Time Series

, , starts our journey into using machine learning for forecasting. A short introduction to machine learning lays down the foundations of what is to come in the next chapters. You will also understand, conceptually, how we can cast a time series problem as a regression problem so that we can use machine learning for it. To close off the chapter, we tease you with the possibility of global forecasting models.

, , shifts gear into a more practical lesson. Using a real-world dataset, you will learn about different feature engineering techniques, such as , , and , which help us formulate a time series problem as a regression problem.

, , continues the practice of exploring different target transformations to accommodate non-stationarity in time series. You will learn techniques such as the and to identify and treat non-stationarity.

, , continues from where the last chapter left off to start training machine learning models on the dataset we have been working on. Using the standard code framework present in the book, you will train models such as , , and on our dataset.

, , takes a step back and explores how we can use multiple forecasts and combine them to create a better forecast. You will explore popular techniques such as , different versions of the , , and to combine the different forecasts we have generated to get a better one.

, , concludes your guided journey into machine learning-enabled forecasting to an exciting and new paradigm—global forecasting models. You will learn how to use global forecasting models and industry-proven techniques to improve their performance, which finally lets you develop scalable and efficient machine learning forecasting systems for thousands of time series.

Part 3—Deep Learning for Time Series

, , we switch tracks and start with a specific type of machine learning—deep learning. In this chapter, we lay the foundations of deep learning by looking at different topics such as , , , and .

, , continues the journey into deep learning by making it specific to time series. Keeping in mind the compositionality of deep learning systems, you will learn about different building blocks with which you can construct a deep learning architecture. The chapter starts off by establishing the and then talks about different blocks such as , , and .

, , strengthens the encoder-decoder architecture that you saw in the previous chapter by showing you a few concrete and common patterns in which you can arrange building blocks to generate forecasts. This is a hands-on chapter where you will be creating forecasts using deep learning-based and different .

, , covers the contemporary topic of using attention to improve deep learning models. The chapter starts off by talking about a generalized attention model with which you will learn different types of attention schemes, such as and . You will also tweak the sequence-to-sequence models from the previous chapter to include attention and then train those models to generate a forecast. The chapter then talks about models, which is a deep learning architecture that relies solely on attention, and then you will use that to generate forecasts as well.

, , tackles yet another important aspect of deep learning-based forecasting. Although the book talked about global forecasting models earlier, there are some differences in how it is implemented for deep learning models. In this chapter, you will learn how to implement global deep learning models and techniques...