Название: Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
Автор: Ivan Gridin
Издательство: BPB Publications
Год: 2022
Страниц: 317
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

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.

Description:

This book is amid at teaching the readers how to apply the 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 programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the 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 learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques.

Artificial Intelligence model-based forecasting has also become a popular research tool for the past decade. The book explores how the latest advances in deep learning can be applied to time series forecasting. Machine Learning and Deep Learning algorithms have obtained a lot of attention in recent years due to their applicability to many real-life problems such as fraud detection, spam email filtering, finance and medical diagnosis. Deep Learning models can automatically detect arbitrary complex mappings from inputs to output. Time series forecasting is challenging. Unlike the more straightforward classification and regression problems, time series forecasting adds the complexity of order dependence between observations. Deep Learning techniques can extract complex hidden patterns in time series datasets that are unreachable to classical statistical methods. That makes deep learning a very promising tool in time series forecasting. This book demonstrates how modern neural networks and the last advances in deep learning can be applied to real-world prediction problems.

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 for:

This 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 Contents:

1. Time Series Problems and Challenges
2. Deep Learning with PyTorch
3. Time Series as Deep Learning Problem
4. Recurrent Neural Networks
5. Advanced Forecasting Models
6. PyTorch Model Tuning with Neural Network Intelligence
7. Applying Deep Learning to Real-world Forecasting Problems
8. PyTorch Forecasting Package
9. What is Next?