A high-precision approach for effective fractal-based similarity search of stochastic non-stationary time series

@article{Sun2008AHA,
  title={A high-precision approach for effective fractal-based similarity search of stochastic non-stationary time series},
  author={Mei-yu Sun},
  journal={2008 International Conference on Machine Learning and Cybernetics},
  year={2008},
  volume={1},
  pages={136-141}
}
Dozens of high level representations of time series have been introduced for data mining in the literature. Traditional dimension reduction methods about similarity query introduce the smoothness to data series in some degree that the important features of time series about non-linearity and fractal are destroyed. In this paper a high-precision approach based on fractal theory and R/S analysis are proposed. The representation is unique in which it allows dimensionality reduction and it also… CONTINUE READING

Figures and Tables from this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 17 REFERENCES

SAX: a Novel Symbolic Representation of Time Series

Jessica Lin, Eamonn Keogh Li Wei, Stefano Lonardi, Experiencing
  • DMKD Journal,2007
  • 2007
VIEW 2 EXCERPTS

and Stefano Lonardi , Experiencing SAX : a Novel Symbolic Representation of Time Series

Jessica Lin, Eamonn Keogh Li Wei
  • DMKD Journal
  • 2007

Discovering similar multidimensional trajectories

  • Proceedings 18th International Conference on Data Engineering
  • 2002
VIEW 1 EXCERPT

Finding Motifs Using Random Projections

  • Journal of Computational Biology
  • 2002
VIEW 1 EXCERPT

Monotony of surprise in large-scale quest for unusual words

A. Apostolico, M E.Bock, S. Lonardi
  • Proceedings of the 6th International conference on research in computational molecular biology,
  • 2002
VIEW 2 EXCERPTS

Similar Papers

Loading similar papers…