Short-term time series forecasting based on the identification of skeleton algebraic sequences

Authors:
Minvydas Ragulskis;Kristina Lukoseviciute;Zenonas Navickas;Rita Palivonaite
Affiliations:
Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-325, Kaunas LT-51368, Lithuania;Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-325, Kaunas LT-51368, Lithuania;Department of Applied Mathematics, Kaunas University of Technology, Studentu 50-325, Kaunas LT-51368, Lithuania;Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-325, Kaunas LT-51368, Lithuania
Venue:
Neurocomputing
Year:
2011

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Abstract

A new short-term time series forecasting method based on the identification of skeleton algebraic sequences is proposed in this paper. The concept of the rank of the Hankel matrix is exploited to detect a base fragment of the time series. Particle swarm optimization and evolutionary algorithms are then used to remove the noise and identify the skeleton algebraic sequence. Numerical experiments with an artificially generated and a real-world time series are used to illustrate the functionality of the proposed method.