Probabilistic discovery of motifs in water level

Authors:
Longzhuang Li;Sreekrishna Nallela
Affiliations:
Dept. of Computing Sciences, Texas A&M University - Corpus Christi;Dept. of Computing Sciences, Texas A&M University - Corpus Christi
Venue:
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Year:
2009

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Abstract

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 projection algorithm is used to discover the unknown time series motifs, which are tested for accuracy by comparing them with the brute force algorithm.