Grammar-Based Feature Generation for Time-Series Prediction
This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the di
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Anthony Mihirana De Silva Philip H.W. Leong
GrammarBased Feature Generation for Time-Series Prediction
SpringerBriefs in Applied Sciences and Technology Computational Intelligence
Series editor Janusz Kacprzyk, Warsaw, Poland
About this Series The series ‘‘Studies in Computational Intelligence’’ (SCI) publishes new developments and advances in the various areas of computational intelligence— quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/10618
Anthony Mihirana De Silva Philip H.W. Leong
Grammar-Based Feature Generation for Time-Series Prediction
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Anthony Mihirana De Silva Electrical and Information Engineering University of Sydney Sydney, NSW Australia
Philip H.W. Leong Electrical and Information Engineering University of Sydney Sydney, NSW Australia
ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-981-287-410-8 ISBN 978-981-287-411-5 (eBook) DOI 10.1007/978-981-287-411-5 Library of Congress Control Number: 2015931243 Springer Singapore Heidelberg New York Dordrecht London © The Author(s) 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer Science+Business Media Singapo
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