Abstract
Traditional procedures for clustering time series are based mostly on crisp hierarchical or partitioning methods. Given that the dynamics of a time series may change over time, a time series might display patterns that may enable it to belong to one cluster over one period while over another period, its pattern may be more consistent with those in another cluster. The traditional clustering procedures are unable to identify the changing patterns over time. However, clustering based on fuzzy logic will be able to detect the switching patterns from one time period to another thus enabling some time series to simultaneously belong to more than one cluster. In particular, this paper proposes a fuzzy approach to the clustering of time series based on their variances through wavelet decomposition. We will show that this approach will distinguish between time series with different patterns in variability as well identifying time series with switching patterns in variability.
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Ann Maharaj, E., D’Urso, P. & Galagedera, D.U.A. Wavelet-based Fuzzy Clustering of Time Series. J Classif 27, 231–275 (2010). https://doi.org/10.1007/s00357-010-9058-4
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DOI: https://doi.org/10.1007/s00357-010-9058-4