Abstract
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 for time series clustering, and is shown to yield useful and robust clustering.
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Wang, X., Smith, K.A., Hyndman, R.J. (2005). Dimension Reduction for Clustering Time Series Using Global Characteristics. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428862_108
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DOI: https://doi.org/10.1007/11428862_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26044-8
Online ISBN: 978-3-540-32118-7
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Time Series
- Lyapunov Exponent
- Dimension Reduction Method
- Cluster Time Series
- Nonlinear Time Series Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.