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<meta content="Allen, D.W., &amp; Taylor, J.G. (1994). Learning Time Series by Neural Networks. Proceedings of the International Conference on Neural Networks, Sorrento, Italy, Marinaro, M. &amp; Morasso, P. (Eds.), Berlin: Springer, 529-532.
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World Wide Web Resources (financial time series databases)
www.ics.uci.edu/~mlearn/MLrepository.html (Machine Learning Databases)
www.abs.gov.au/ausstats/abs@.nsf/w2.3 (Australian Bureau of Statistics 19. free sample data)
www.nla.gov.au/oz/stats.html (National Library of Australia e.g. financial indicators)
Author Notes
Requests for reprints should be sent to Professor John Fulcher, School of IT &amp; Computer Science, University of Wollongong NSW 2522, Australia (john@uow.edu.au).
We would like to acknowledge the financial assistance of the following organizations in our development of Higher-Order Neural Networks: Societe International de Telecommunications Aeronautique, Fujitsu Research Laboratories, Japan, the US National Research Council and the Australian Research Council.
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    <h1 class="ep_tm_pagetitle">Application of Higher-Order Neural Networks to Financial Time-Series Prediction</h1>
    <p style="margin-bottom: 1em" class="not_ep_block"><span class="person_name">Fulcher, John</span> and <span class="person_name">Zhang, Ming</span> and <span class="person_name">Xu, Shuxiang</span> (2006) <xhtml:em>Application of Higher-Order Neural Networks to Financial Time-Series Prediction.</xhtml:em> In: Artificial Neural Networks in Finance and Manufacturing. . Idea Group Publishing, Hershey, PA. ISBN 1591406714</p><p style="margin-bottom: 1em" class="not_ep_block"></p><table style="margin-bottom: 1em" class="not_ep_block"><tr><td valign="top" style="text-align:center"><a onmouseover="EPJS_ShowPreview( event, 'doc_preview_650' );" href="http://eprints.utas.edu.au/638/1/kamruzzaman_Xu_chapter.pdf" onmouseout="EPJS_HidePreview( event, 'doc_preview_650' );"><img alt="[img]" src="http://eprints.utas.edu.au/style/images/fileicons/application_pdf.png" class="ep_doc_icon" border="0" /></a><div class="ep_preview" id="doc_preview_650"><table><tr><td><img alt="" src="http://eprints.utas.edu.au/638/thumbnails/1/preview.png" class="ep_preview_image" border="0" /><div class="ep_preview_title">Preview</div></td></tr></table></div></td><td valign="top"><a href="http://eprints.utas.edu.au/638/1/kamruzzaman_Xu_chapter.pdf"><span class="ep_document_citation">PDF</span></a> - Requires a PDF viewer<br />1757Kb</td></tr></table><div class="not_ep_block"><h2>Abstract</h2><p style="padding-bottom: 16px; text-align: left; margin: 1em auto 0em auto">Financial time series data is characterized by non-linearities, discontinuities and high frequency, multi-polynomial components. Not surprisingly, conventional Artificial Neural Networks (ANNs) have difficulty in modelling such complex data. A more appropriate approach is to apply Higher-Order ANNs, which are capable of extracting higher order polynomial coefficients in the data. Moreover, since there is a one-to-one correspondence between network weights and polynomial coefficients, HONNs (unlike ANNs generally) can be considered open-, rather than 'closed box' solutions, and thus hold more appeal to the financial community. After developing Polynomial and Trigonometric HONNs, we introduce the concept of HONN groups. The latter incorporate piecewise continuous activation functions and thresholds, and as a result  are capable of modelling discontinuous (piecewise continuous) data, and what's more to any degree of accuracy. Several other PHONN variants are also described. The performance of P(T)HONNs and HONN groups on representative financial time series is described (credit ratings and exchange rates). In short, HONNs offer roughly twice the performance of MLP/BP on financial time series prediction, and HONN groups around 10% further improvement.</p></div><table style="margin-bottom: 1em" cellpadding="3" class="not_ep_block" border="0"><tr><th valign="top" class="ep_row">Item Type:</th><td valign="top" class="ep_row">Book Chapter</td></tr><tr><th valign="top" class="ep_row">Keywords:</th><td valign="top" class="ep_row">artificial neural networks, financial time series, higher-order artificial neural networks, artificial neural network group</td></tr><tr><th valign="top" class="ep_row">Subjects:</th><td valign="top" class="ep_row"><a href="http://eprints.utas.edu.au/view/subjects/280212.html">280000 Information, Computing and Communication Sciences &gt; 280200 Artificial Intelligence and Signal and Image Processing &gt; 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic</a></td></tr><tr><th valign="top" class="ep_row">ID Code:</th><td valign="top" class="ep_row">638</td></tr><tr><th valign="top" class="ep_row">Deposited By:</th><td valign="top" class="ep_row"><span class="ep_name_citation"><span class="person_name">DR SHUXIANG XU</span></span></td></tr><tr><th valign="top" class="ep_row">Deposited On:</th><td valign="top" class="ep_row">01 Feb 2007</td></tr><tr><th valign="top" class="ep_row">Last Modified:</th><td valign="top" class="ep_row">09 Jan 2008 02:30</td></tr><tr><th valign="top" class="ep_row">ePrint Statistics:</th><td valign="top" class="ep_row"><a target="ePrintStats" href="/es/index.php?action=show_detail_eprint;id=638;">View statistics for this ePrint</a></td></tr></table><p align="right">Repository Staff Only: <a href="http://eprints.utas.edu.au/cgi/users/home?screen=EPrint::View&amp;eprintid=638">item control page</a></p>
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