Dimension Reduction for Clustering Time Series Using Global Characteristics

@inproceedings{Wang2005DimensionRF,
  title={Dimension Reduction for Clustering Time Series Using Global Characteristics},
  author={Xiaozhe Wang and Kate Smith-Miles and Rob J. Hyndman},
  booktitle={International Conference on Computational Science},
  year={2005}
}
Existing methods for time series clustering rely on the actual data values can become impractical since the methods do not easily handle dataset with high dimensionality, missing value, or different lengths. In this paper, a dimension reduction method is proposed that replaces the raw data with some global measures of time series characteristics. These measures are then clustered using a self-organizing map. The proposed approach has been tested using benchmark time series previously reported… CONTINUE READING

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  • 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP)
  • 2015
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References

Publications referenced by this paper.
SHOWING 1-9 OF 9 REFERENCES

S.:Supporting Content-based Searches on Time Series via Approximation. In: the 12th international conference on scientific and statistical database

C. Wang, X. Wang
  • 2000
VIEW 1 EXCERPT

Supporting content-based searches on time series via approximation

  • Proceedings. 12th International Conference on Scientific and Statistica Database Management
  • 2000

Self-Organizing Maps

  • Springer Series in Information Sciences
  • 1995
VIEW 1 EXCERPT

Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource (With Discussion)

J. Haslett, A. E. Raftery
  • Applied Statistics,
  • 1989
VIEW 1 EXCERPT