Time-series prediction using self-organising mixture autoregressive network

He Ni, Hujun Yin

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

    Abstract

    In the past few years, various variants of the self-organising map (SOM) have been proposed to extend its ability for modelling timeseries or temporal sequence. Most of them, however, have little connection to, or are over-simplified, autoregressive (AR) models. In this paper, a new extension termed, self-organising mixture autoregressive (SOMAR) network is proposed to topologically cluster time-series segments into underlying generating AR models. It uses autocorrelation values as the similarity measure between the model and the time-series segments. Such networks can be used for modelling nonstationary timeseries. Experiments on predicting artificial time-series (Mackey-Glass) and real-world data (foreign exchange rates) are presented and results show that the proposed SOMAR network is a viable and superior to other SOM-based approaches. © Springer-Verlag Berlin Heidelberg 2007.
    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.
    PublisherSpringer Nature
    Pages1000-1009
    Number of pages9
    Volume4881
    ISBN (Print)9783540772255
    Publication statusPublished - 2007
    Event8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 - Birmingham
    Duration: 1 Jul 2007 → …

    Conference

    Conference8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007
    CityBirmingham
    Period1/07/07 → …

    Fingerprint

    Dive into the research topics of 'Time-series prediction using self-organising mixture autoregressive network'. Together they form a unique fingerprint.

    Cite this