Self-organising mixture autoregressive model for non-stationary time series modelling

He Ni, Hujun Yin

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    Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches. © 2008 World Scientific Publishing Company.
    Original languageEnglish
    Pages (from-to)469-480
    Number of pages11
    JournalInternational Journal of Neural Systems
    Issue number6
    Publication statusPublished - Dec 2008


    • Autoregressive models
    • Mixture of temporal models
    • Non-stationarity
    • Self-organising map
    • Time series


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