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: Neural Networks for Time Series Forecasting with R 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.
• Worked examples in R you can easily follow and immediately implement.
• Ideas you can actually use and try out with your own data.
TAKE THE SHORTCUT: Neural Networks for Time Series Forecasting with R was written for people who want to get up to speed as quickly as possible.
In this book you will 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.
QUICK AND EASY: 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.
Overview of Book
Chapter 1: The Characteristics of Time Series Data
Chapter 2: Feed-Forward Neural Networks Explained
Chapter 3: Recurrent Neural Networks
Chapter 4: Long Short-Term Memory Recurrent Neural Network
Chapter 5: Gated Recurrent Unit Neural Networks
Chapter 6: Elman Neural Networks
Chapter 7: Jordan Neural Networks
Chapter 8: General Method of Data Handling Type Neural Networks
Hands on Projects Include:
• Predicting The Number of New Cases of Escherichia coli.
• Forecasting the Whole Bird Spot Price of Chicken.
• Predicting Eye Movements.
• Forecasting Electrocardiogram Activity.
• Historical Wheat Price Forecasting.
• Modeling Air Temperature.
• Forecasting Monthly Sunspot Numbers.
GET STARTED TODAY! 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!