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DEEP TIME SERIES FORECASTING With PHon An Intuitive Introduction to Deep learn ing for applied Time series Modeling Dr N.d lewis Copyright o 2016 by N.D. Lewis All rights reserved. No part of this publication may be reproduced, dis- tributed, or transmitted in any form or by any means, including photo- copying, recording, or other electronic or mechanical methods, without the prior written permission of the author, except in the case of brief quo tations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, contact the author Disclaimer: Although the author and publisher have made every effort to ensure that the information in this book was correct at press time, the author and publisher do not assume and hereby disclaim any liability to any party for any loss, damage, or disruption caused by errors or omissions whether such errors or omissions result from negligence, accident, or any other cause Ordering Information: Quantity sales. Special discounts are available on quantity purchases by corporations, associations, and others. For details, email: info@NigelDLewis com Image photography by Deanna Lewis with helpful assistance om naomi ewis ISBN-13:978-1540809087 ISBN-10:1540809080 欢迎加入非盈利 Pyt hor编程学习交流α群783462347,群里免费提供5004本 Pyt ho书籍! Contents Acknowledgements Preface How to get the absolute most possible benefit from this book Getting Python Learning Python Using Packages Additional Resources to Check Out 1 The Characteristics of Time Series Data Simplified Understanding the Data Generating Mechanism 13345779 Generating a Simple Time Series using Python Randomness and Reproducibility The Importance of Temporal Order The Ultimate goal For Additional Exploration 15 2 Deep Neural Networks Explained 17 What is a Neural Network? The role of neuron Deep Learning in a Nutshell Generating Data for use with a Deep Neural Network Exploring the Sample data 22 A Super Easy Deep Neural Network Tool mat Translating Sample Data into a Suitable Fo 25 26 Additional resources to Check out 2 30 3 Deep Neural Networks for Time Series Forecasting the Easy Way 31 Getting the Data from the Internet 31 Cleaning up Downloaded Spreadsheet Files 33 Understanding Activation Functions 36 How to Scale the Input attributes 39 Assessing Partial Autocorrelation A Neural Network Architecture for Time Series Forecasting 45 Additional Resources to Check Out 49 欢迎加入非盈利 Pyt hor编程学习交流α群783462347,群里免费提供5004本 Pyt ho书籍! 4 A Simple Way to Incorporate Additional Attributes in Your Model 51 Working with Additional Attributes 51 The Working of the Neuron Simplified How a Neural network learns Gradient Descent Clarified 58 How to Easily Specify a Model 59 Choosing a Learning Rate The efficient Way to Run Your Model Additional Resources to check Out 66 5 The Simple recurrent Neural Network 67 Why Use Keras What is a Recurrent Neural Network? Gain Clarity on the Role of the Delay Units 71 Follow this Approach to Create Your Train and Test Sets Parameter Sharing Clarified Understand Backpropagation Through Time 73 A Complete Intuitive Guide to Momentum 76 How to Benefit from Mini Batching Additional resources to Check Out 81 6 Elman Neural networks 83 Prepare You Data for Easy Use 84 How to Model a Complex mathematical Relationship with No Knowledge Use this Python library for Rapid Results Exploring the Error Surface A Super Simple Way to Fit the Model 91 Additional Resources to Check Out 7 Jordan neural networks 95 The Fastest Path to Data Preparation A Straightforward Module for Jordan Neural Networks Assessing Model Fit and Performance Additional Resources to check Out 100 8 Nonlinear Auto-regressive Network with Exogenous Inputs 103 What is a NaRX Network? 103 Working with Macroeconomic Variables Spreadsheet Files Made Easy with Panda 105 107 Python and Pandas Data Types A Tool for Rapid narX Model Construction 113 How to run the model 115 Additional Resources to Check Out 117 9 Long Short-Term Memory Recurrent Neural Network 119 Cyclical Patterns in Time Series Data 119 What is an LSTM? Efficiently Explore and Quickly Understand Data The Lstm memory block in a Nutshell 127 Straightforward Data Transformation for the Train and Test Sets 128 Clarify the Role of Gates 130 Understand the Constant Error Carousel 131 Specifying a LSTM Model the Easy W 132 Shuffling Examples to Improve generalization 136 A Note on Vanishing Gradients 欢迎加入非盈利 Pyt hor编程学习交流α群783462347,群里免费提供5004本 Pyt ho书籍! Follow these Steps to build a Stateful LSTM 139 Additional resources to Check Out 144 10 Gated Recurrent unit 145 The Gated Recurrent Unit in a nutshell A Simple Approach to Gated Recurrent Unit Construction 148 A Quick Recap 150 How to Use Multiple Time Steps 151 Additional resources to Check Out 154 11 Forecasting Multiple Outputs 155 Working with Zipped Files 156 How to Work with Multiple Targets 159 Creation of Hand Crafted Features 161 Model Specification and Fit 163 Additional Resources to Check Out 12 Strategies to Build Superior Models 169 Revisiting the UK Unemployment Rate Economic Data 169 Limitations of the sigmoid Activation Function 171 One Activation Function You Need to Add to Your Deep Learning Toolkit.. 17 Try This Simple Idea to Enhance Success 176 A Simple Plan for Early Stopping 180 Additional Resources to Check Out 184 ndex 189 欢迎加入非盈利 Pyt hor编程学习交流α群783462347,群里免费提供5004本 Pyt ho书籍! 欢迎加入非盈利 Pyt hor编程学习交流α群783462347,群里免费提供5004本 Pyt ho书籍! Dedicated to Angela, wife, friend and mother extraordinaire 欢迎加入非盈利 Pyt hor编程学习交流α群783462347,群里免费提供5004本 Pyt ho书籍!

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