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Deep.Learning.in.Time.Series.Analysis.Sanet.st.pdf
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Deep.Learning.in.Time.Series.Analysis.Sanet.st
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Deep Learning in Time
Series Analysis
Arash Gharehbaghi
Researcher, School of Information Technology
Halmstad University, Halmstad, Sweden
A SCIENCE PUBLISHERS BOOK
p,
A SCIENCE PUBLISHERS BOOK
p,
First edition published 2023
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
© 2023 Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, LLC
Reasonable efforts have been made to publish reliable data and information, but the author and
publisher cannot assume responsibility for the validity of all materials or the consequences of
their use. The authors and publishers have attempted to trace the copyright holders of all material
reproduced in this publication and apologize to copyright holders if permission to publish in this
form has not been obtained. If any copyright material has not been acknowledged please write
and let us know so we may rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced,
transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or
hereafter invented, including photocopying, microfilming, and recording, or in any information
storage or retrieval system, without written permission from the publishers.
For permission to photocopy or use material electronically from this work, access
www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive,
Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact
mpkbookspermissions@tandf.co.uk
Trademark notice: Product or corporate names may be trademarks or registered trademarks and are
used only for identification and explanation without intent to infringe.
Library of Congress Cataloging‑in‑Publication Data (applied for)
ISBN: 978-0-367-32178-9 (hbk)
ISBN: 978-1-032-41886-5 (pbk)
ISBN: 978-0-429-32125-2 (ebk)
DOI: 10.1201/9780429321252
Typeset in Times New Roman
by Radiant Productions
Cover illustration courtesy of Reza Gharehbaghi
To Shabnam, Anita and Parsa.
I am delighted to introduce the first book on deep learning for time series analysis
in which analysis of cyclic time series is profoundly addressed along with the
theories. This idea was developed within a structure of a hybrid model where the
experimental results showed its outperformance against the baselines of neural
network-based methods. It was later improved by incorporating deep learning
structures of a time growing neural network, the network which was previously
introduced by us as a strong alternative to multilayer perceptron and time-
delayed neural networks, into a multi-scale learning structure. The idea of cyclic
learning is applicable to many natural learning where the phenomena exhibit
cyclic behaviours. Physiological characteristics of the human body emanate
cyclic activities in many cases such as cardiac and respiratory activities. The idea
of cyclic learning has received interest from researchers from various domains of
engineering and science.
Realistic validation of machine learning methods is a crucial task. A realistic
validation method must provide sufficient outcomes to project capability of a
machine learning method in terms of its risks in reproducibility of the results
in conjunction with the improvement of the results when the machine learning
method is being trained by a richer dataset. These validation capabilities are
considered in the A-Test method. As a validation method, A-Test has received
recognition from different engineering domains. These methods are likely to
become strong machine learning methods, especially for applications with a
small size of the learning data.
Foreword
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