Preface

The writing of this book was motivated by the increasing demand for practical approaches to time series analysis and forecasting. Organizations across various sectors rely on time series analysis to gain insights into their operations. By leveraging time series, these organizations can make informed decisions and optimize their performance. Accurate forecasts are valuable assets across many application domains, such as retail or economics. These predictions help reduce uncertainty and enable better planning of operations. Overall, time series analysis is a valuable skill for data scientists to understand and extract meaningful insights from collections of observations that evolve over time.

Meanwhile, deep learning is driving recent important scientific and technological advances. It is a subset of machine learning and artificial intelligence where models are based on artificial neural networks. Deep learning is foundational to many technologies we use and hear about today, including ChatGPT, self-driving cars, and advanced image recognition tools. At the same time, deep learning methods require significant technical expertise to produce meaningful results.

This book guides machine learning practitioners and enthusiasts interested in applying deep learning to learn from time series data. We present clear and easy-to-follow code recipes for applying deep learning to time series data. While the content is tailored for beginners, more seasoned machine learning professionals can also find value in the nuances of more advanced techniques. The book presents a learn-by-doing approach to ensure that you not only understand the main concepts but also know how to apply them effectively.

The book covers several popular time series problems, such as forecasting, anomaly detection, and classification. These tasks are solved with different deep neural network architectures, including convolutional neural networks or transformers. We use the PyTorch ecosystem, a popular deep learning framework based on Python.

By the end of this book, you’ll be able to solve different time series tasks using deep learning methods.