Build real-world applications with Python to learn how to apply the power of Transformers to Time Series data Key Features Learn how to apply the technology to time series that made LLMs a turning point in the world of AI Unlock mastering levels of Transformers using leading Python packages, PyTorch and Tensorflow Get hands-on experience with real data sets to develop your skills quickly Book DescriptionGenerative AI has profoundly changed the world, and Transformers are a crucial instrument in this process. However, the application of Transformers for time series hasn't been widely adopted yet, despite the immense potential in this field. Transformers, among other things, possess the ability to identify long-range dependencies and interactions in the data. In the Transformers for Time Series Forecasting book, the most recent research findings are presented in a highly practical fashion. Utilizing real-life projects and employing PyTorch and TensorFlow, the reader is guided through various use cases. Starting with the most commonly utilised applications for time series data, such as forecasting and classification, the book introduces the reader to both the theory and implementation. Later, more specialised cases are covered, including anomaly detection, event forecasting, and spatio-temporal modelling. The final chapters introduce how to improve these algorithms further, what the best practices are, how to optimise with hyperparameter tuning techniques and architecture-level modifications. Lastly, we discuss how to scale transformer-based solutions when dealing with large amounts of data.What you will learn Understand challenges in time series analysis and advantages of using Transformers Learn how to build a Transformer model for time series with Python and leading libraries Acquire practical skills and knowledge to effectively forecast time series data using Transformer models Explore a real-world case study that showcases the Transformer model in time series classification Master the art of preparing data and building Transformer model for time series classification Gain insights into event forecasting, spatio-temporal modelling and anomaly detection with Transformers Learn about best practices and optimisation techniques for Transformers Become familiar with distributed computing techniques for handling large-scale time series data Who this book is forIf you are a data scientist, machine learning engineer, or researcher who is constantly looking to upskill, or if you specifically deal with time series and want to harness the efficiency of Large Language Models, then this book is for you! This book is for readers who have a basic understanding of Python, machine learning and deep learning concepts.