Algebraic segmentation of short nonstationary time series based on evolutionary prediction algorithms

• Published in: Neurocomputing (Amsterdam) 2013-12, Vol.121, p.354-364
• Main Authors: Palivonaite, Rita, Lukoseviciute, Kristina, Ragulskis, Minvydas
• Format: Article
• Language: English
• Subjects: Acceptability; Algorithmics. Computability. Computer arithmetics; Applied sciences; Computer science; control theory; systems; Exact sciences and technology; Rank of a sequence; Segmentation; Theoretical computing; Time series prediction
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2013.05.013

Algebraic segmentation of short nonstationary time series is presented in this paper. The proposed algorithm is based on the algebraic one step-forward predictor which is used to identify a temporal near-optimal algebraic model of the real-world time series. A combinatorial algorithm is used to identify intervals where prediction errors are lower than a predefined level of acceptable accuracy. Special deterministic strategy is developed for the selection of this acceptable level of prediction accuracy and is individually determined for every time series. The nonparametric identification of quasistationary segments is performed without the employment of any statistical estimator. Several standard real-world time series are used to demonstrate the efficiency of the proposed technique. •Algebraic segmentation of short nonstationary time series is presented in this paper.•The proposed algorithm is based on the algebraic one step-forward predictor which is used to identify a temporal near-optimal algebraic model of the real-world time series.•The nonparametric identification of quasistationary segments is performed without the employment of any statistical estimator.