Chaotic Feature Selection and Reconstruction in Time Series Prediction

  • Shamina Hussein
  • Rohitash ChandraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)


The challenge in feature selection for time series lies in achieving similar prediction performance when compared with the original dataset. The method has to ensure that important information has not been lost by with feature selection for data reduction. We present a chaotic feature selection and reconstruction method based on statistical analysis for time series prediction. The method can also be viewed as a way for reduction of data through selection of most relevant features with the hope of reducing training time for learning algorithms. We employ cooperative neuro-evolution as a machine learning tool to evaluate the performance of the proposed method. The results show that our method gives a data reduction of up to 42 % with a similar performance when compared to the literature.


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Mathematical and Computing SciencesFiji National UniversitySuvaFiji
  2. 2.Artificial Intelligence and Cybernetics Research Group Software FoundationNausoriFiji

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