Deep Learning for Time Series Cookbook

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.

What you will learn

• Grasp the core of time series analysis and unleash its power using Python
• Understand PyTorch and how to use it to build deep learning models
• Discover how to transform a time series for training transformers
• Understand how to deal with various time series characteristics
• Tackle forecasting problems, involving univariate or multivariate data
• Master time series classification with residual and convolutional neural networks
• Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs)

• # Data Processing