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Article

Hierarchical mixtures of autoregressive models for time-series modeling

Authors:

Alberto SuárezAuthors Info & Claims

ICANN/ICONIP'03: Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing

Pages 597 - 604

Published: 26 June 2003 Publication History

Abstract

A hierarchical mixture of autoregressive (AR) models is proposed for the analysis of nonlinear time-series. The model is a decision tree with soft sigmoidal splits at the inner nodes and linear autoregressive models at the leaves. The global prediction of the mixture is a weighted average of the partial predictions from each of the AR models. The weights in this average are computed by the application of the hierarchy of soft splits at the inner nodes of the tree on the input, which consists in the vector of the delayed values of the time series. The weights can be interpreted as a priori probabilities that an example is generated by the AR model at that leaf. As an illustration of the flexibility and robustness of the models generated by these mixtures, an application to the analysis of a financial time-series is presented.

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Cited By

Hernández-Lobato JSuárez A(2006)Competitive and collaborative mixtures of experts for financial risk analysisProceedings of the 16th international conference on Artificial Neural Networks - Volume Part II10.1007/11840930_72(691-700)Online publication date: 10-Sep-2006
https://dl.acm.org/doi/10.1007/11840930_72

Hierarchical mixtures of autoregressive models for time-series modeling

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ICANN/ICONIP'03: Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing

June 2003

1187 pages

ISBN:3540404082

Editors:
Okyay Kaynak
Bogazici University, Istanbul, Turkey
,
Ethem Alpaydin
Bogazici University, Istanbul, Turkey
,
Erkki Oja
Helsinki University of Technology, Laboratory of Computer and Information Science, HUT, Finland
,
Lei Xu
The Chinese University of Hong Kong, Department of Computer Science and Engineering, Shatin, Hong Kong

Sponsors

European Office of Aerospace Research and Development of the USAF
TÜBITAK: Turkish Scientific and Technical Research Council
Boǧaziçi University Foundation

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 June 2003

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Cited By

Hernández-Lobato JSuárez A(2006)Competitive and collaborative mixtures of experts for financial risk analysisProceedings of the 16th international conference on Artificial Neural Networks - Volume Part II10.1007/11840930_72(691-700)Online publication date: 10-Sep-2006
https://dl.acm.org/doi/10.1007/11840930_72

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