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The discovery of water level time series motifs is of much importance to improve the water level predictions. These predictions thereby are useful to the shipping industry, people living in the coastal areas, and even for emergency evacuation in case of a hurricane. In this paper, symbolic aggregate approximation (SAX) is employed to index and reduce the dimension of the time series, and the random...
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