Non-Linear Time Series Clustering based on Non-Parametric Forecast Densities Articles uri icon

publication date

  • November 2010

start page

  • 2850

end page

  • 2865

issue

  • 11

volume

  • 54

international standard serial number (ISSN)

  • 0167-9473

electronic international standard serial number (EISSN)

  • 1872-7352

abstract

  • The problem of clustering time series is studied for a general class of non-parametric autoregressive models. The dissimilarity between two time series is based on comparing their full forecast densities at a given
    horizon. In particular, two functional distances are considered: L1 and
    L2. As the forecast densities are unknown, they are approximated using a
    bootstrap procedure that mimics the underlying generating processes
    without assuming any parametric model for the true autoregressive
    structure of the series. The estimated forecast densities are then used
    to construct the dissimilarity matrix and hence to perform clustering.
    Asymptotic properties of the proposed method are provided and an
    extensive simulation study is carried out. The results show the good
    behavior of the procedure for a wide variety of nonlinear autoregressive
    models and its robustness to non-Gaussian innovations. Finally, the
    proposed methodology is applied to a real dataset involving economic
    time series.