Abstract
The self-organizing mixture autoregressive (SOMAR) model regards a time series as a mixture of regressive processes. A self-organizing algorithm is used with the LMS algorithm to learn the parameters of these regressive models. The self-organizing map is used to simplify the mixture as a winner-take-all selection of local models, combined with an autocorrelation coefficient based measure as the similarity measure for identifying correct local models. The SOMAR has been shown previously being able to uncover underlying autoregressive processes from a mixture. This paper proposes a generalized SOMAR that fully considers the mixing mechanism and individual model variances that make modeling and prediction more accurate for non-stationary time series. Experiments on both benchmark and financial time series are presented. The results demonstrate the superiority of the proposed method over other time-series modeling techniques on a range of performance measures.
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Yin, H., Ni, H. (2009). Generalized Self-Organizing Mixture Autoregressive Model. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_40
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DOI: https://doi.org/10.1007/978-3-642-02397-2_40
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