OBST-based segmentation approach to financial time series

@article{Si2013OBSTbasedSA,
  title={OBST-based segmentation approach to financial time series},
  author={Yain-Whar Si and Jiangling Yin},
  journal={Eng. Appl. of AI},
  year={2013},
  volume={26},
  pages={2581-2596}
}
Financial time series data are large in size and dynamic and non-linear in nature. Segmentation is often performed as a pre-processing step for locating technical patterns in financial time series. In this paper, we propose a segmentation method based on Turning Points (TPs). The proposed method selects TPs from the financial time series in question based on their degree of importance. A TP's degree of importance is calculated on the basis of its contribution to the preservation of the trends… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 46 REFERENCES

Variable length queries for time series data

  • Proceedings 17th International Conference on Data Engineering
  • 2001
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

A New Segmentation Algorithm to Stock Time Series Based on PIP Approach

  • 2007 International Conference on Wireless Communications, Networking and Mobile Computing
  • 2007
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL