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Deep Learning for Time Series Cookbook
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Deep Learning for Time Series Cookbook
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Overview of this book
Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise.
This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions.
By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.
Table of Contents (12 chapters)
Preface
Free Chapter
Chapter 1: Getting Started with Time Series
Chapter 2: Getting Started with PyTorch
Chapter 3: Univariate Time Series Forecasting
Chapter 4: Forecasting with PyTorch Lightning
Chapter 5: Global Forecasting Models
Chapter 6: Advanced Deep Learning Architectures for Time Series Forecasting
Chapter 7: Probabilistic Time Series Forecasting
Chapter 8: Deep Learning for Time Series Classification
Chapter 9: Deep Learning for Time Series Anomaly Detection
Index
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