Sparse model from optimal nonuniform embedding of time series

Chetan Nichkawde

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)


    An approach to obtaining a parsimonious polynomial model from time series is proposed. An optimal minimal nonuniform time series embedding schema is used to obtain a time delay kernel. This scheme recursively optimizes an objective functional that eliminates a maximum number of false nearest neighbors between successive state space reconstruction cycles. A polynomial basis is then constructed from this time delay kernel. A sparse model from this polynomial basis is obtained by solving a regularized least squares problem. The constraint satisfaction problem is made computationally tractable by keeping the ratio between the number of constraints to the number of variables small by using fewer samples spanning all regions of the reconstructed state space. This helps the structure selection process from an exponentially large combinatorial search space. A forward stagewise algorithm is then used for fast discovery of the optimization path. Results are presented for the Mackey-Glass system.
    Original languageEnglish
    Article number042911
    Pages (from-to)1-9
    Number of pages9
    JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
    Issue number4
    Publication statusPublished - 18 Apr 2014


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