Neural Networks For Time Series Forecasting With R
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Neural Networks for Time Series Forecasting with R
Finally, A Blueprint for Neural Network Time Series Forecasting with R! Neural Networks for Time Series Forecasting with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating neural network models for time series forecasting with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE REQUIRED: This book uses plain language rather than a ton of equations; I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to try neural networks for time series forecasting for yourself. YOUR PERSONAL BLUE PRINT: Through a simple to follow step by step process, you will learn how to build neural network time series forecasting models using R. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. THIS BOOK IS FOR YOU IF YOU WANT: Explanations rather than mathematical derivation Practical illustrations that use real data. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use and try on your own data. TAKE THE SHORTCUT: This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. In this book you will learn how to: YOU'LL LEARN HOW TO: Unleash the power of Long Short-Term Memory Neural Networks. Develop hands on skills using the Gated Recurrent Unit Neural Network. Design successful applications with Recurrent Neural Networks. Deploy Jordan and Elman Partially Recurrent Neural Networks. Adapt Deep Neural Networks for Time Series Forecasting. Master the General Method of Data Handling Type Neural Networks. For each neural network model, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. Everything you need to get started is contained within this book. Neural Networks for Time Series Forecasting with R is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!
Recurrent Neural Networks for Short-Term Load Forecasting
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Neural Network Time Series
Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.