Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions

by Ivan Gridin

Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks Key Features● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts.● Includes practical demonstration of robust deep learning prediction models with exciting use-cases.● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence.DescriptionThis book aims to teach the readers how to apply deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch.The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the program has been developed. You will try to use machine learning to identify the patterns that can help us forecast future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task.Finally, by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learned throughout the book. This book also offers another great way of mastering deep learning and its various techniques.What you will learn● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics.● Learn the basics of neural architecture search with Neural Network Intelligence.● Combine standard statistical analysis methods with deep learning approaches.● Automate the search for optimal predictive architecture.● Design your custom neural network architecture for specific tasks.● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes.Who this book is forThis book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed.Table of Contents1. Time Series Problems and Challenges2. Deep Learning with PyTorch3. Time Series as Deep Learning Problem4. Recurrent Neural Networks5. Advanced Forecasting Models6. PyTorch Model Tuning with Neural Network Intelligence7. Applying Deep Learning to Real-world Forecasting Problems8. PyTorch Forecasting Package9. What is Next?

421 pages, Kindle Edition

Published October 15, 2021

Reviews

[Andrew Thompson · Rating 5 out of 5]

The author has a clean approach, and a clear understanding of mathematical concepts. I will copy this approach in my own thinking from now on.

It's not a beginner text, so you do need to apply yourself and work a bit, but your work is rewarded.

[Reinis Bekeris · Rating 2 out of 5]

Linear explanation path, but the code is hard to work with. Not because it’s complicated but because it’s inconsistent between chapters (inheritances).