On wavelet decomposition of uncertain time series data sets

In this paper, we will explore the construction of wavelet decompositions of uncertain data. Uncertain representations of data sets require significantly more space, and it is therefore even more important to construct compressed representations for such cases. We will use a hierarchical optimization technique in order to construct the most effective partitioning for our wavelet representation. We explore two different schemes which optimize the uncertainty in the resulting representation. We will show that the incorporation of uncertainty into the design of the wavelet representations significantly improves the compression rate of the representation. We present experimental results illustrating the effectiveness of our approach.

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