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Ego autem et domus
mea serviemus Domino.
DEEP TIME SERIES
FORECASTING
With PYTHON
An Intuitive Introduction to Deep Learn-
ing for Applied Time Series Modeling
Dr. N.D Lewis
Copyright © 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
at: www.AusCov.com.
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
from Naomi Lewis.
ISBN-13: 978-1540809087
ISBN-10: 1540809080
Contents
Acknowledgements iii
Preface viii
How to Get the Absolute Most Possible Benefit from this Book 1
Getting Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Learning Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Using Packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Additional Resources to Check Out . . . . . . . . . . . . . . . . . . . . . . . . 5
1 The Characteristics of Time Series Data Simplified 7
Understanding the Data Generating Mechanism . . . . . . . . . . . . . . . . . 7
Generating a Simple Time Series using Python . . . . . . . . . . . . . . . . . 9
Randomness and Reproducibility . . . . . . . . . . . . . . . . . . . . . . . . 12
The Importance of Temporal Order . . . . . . . . . . . . . . . . . . . . . . . . 13
The Ultimate Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
For Additional Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2 Deep Neural Networks Explained 17
What is a Neural Network? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
The Role of Neuron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Deep Learning in a Nutshell . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Generating Data for use with a Deep Neural Network . . . . . . . . . . . . . 21
Exploring the Sample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Translating Sample Data into a Suitable Format . . . . . . . . . . . . . . . . 25
A Super Easy Deep Neural Network Tool . . . . . . . . . . . . . . . . . . . . 26
Assessing Model Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Additional Resources to Check Out . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . 42
A Neural Network Architecture for Time Series Forecasting . . . . . . . . . . 45
Additional Resources to Check Out . . . . . . . . . . . . . . . . . . . . . . . . 49
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