Author Description

Deep learning (DL) is a cutting-edge approach to learning from data. While it has taken the areas of computer vision and natural language processing by storm, its application to time-series forecasting is a more recent phenomenon and remains challenging for both new and experienced practitioners. To develop the best time series models for a real-world problem, it is essential to have not only a thorough understanding of the time series data but also a solid grasp of DL models themselves. This book investigates time series structures and the DL approaches that can address the variety of challenges they present to practitioners in industry. In this book, you will gain insights from a variety of perspectives, both from the data and the models. You will learn about the complexities of real-world time series data, explore the different problem settings for time series analysis, touch upon the foundation of DL models for time series, and practice end-to-end time series analysis projects when DL works; the authors believe in choosing the best tool for the problem, so traditional methods are never far from our minds. A GitHub repository with coding examples will be provided to support your journey. By the end of this book, you will be able to approach almost any time series challenge with an appropriate model that gets you results.