Recurrent self-organising maps and local support vector machine models for exchange rate prediction

He Ni, Hujun Yin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This paper considers the problem of predicting non-linear, non-stationary financial time sequence data, which is often difficult for traditional regressive models. The Self-Organising Map (SOM) is a vector quantisation method that represents statistical data sets in a topology preserving fashion. The method, which uses the Recurrent Self-Organising Map(RSOM) to partition the original data space into several disjointed regions and then uses Support Vector Machines (SVMs) to make the prediction as a regression method. It is model free and does not require a prior knowledge of the data. Experiments show that the method can make certain degree of profits and outperforms the GARCH method. © Springer-Verlag Berlin Heidelberg 2006.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    Place of PublicationBerlin
    PublisherSpringer Nature
    Pages504-511
    Number of pages7
    Volume3973
    ISBN (Print)3540344829, 9783540344827
    DOIs
    Publication statusPublished - 2006
    Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu
    Duration: 1 Jul 2006 → …

    Conference

    Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
    CityChengdu
    Period1/07/06 → …

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