Title :
Probabilistic discovery of motifs in water level
Author :
Li, Longzhuang ; Nallela, Sreekrishna
Author_Institution :
Dept. of Comput. Sci., Texas A&M Univ. - Corpus Christi, Corpus Christi, TX, USA
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.
Keywords :
data mining; time series; water resources; brute force algorithm; emergency evacuation; probabilistic discovery; random projection algorithm; shipping industry; symbolic aggregate approximation; time series motifs; water level; Aggregates; Data mining; Discrete Fourier transforms; Discrete wavelet transforms; Hurricanes; Projection algorithms; Scalability; Sea measurements; Shipbuilding industry; Testing; Convex linear combination; random projection algorithm; symbolic aggregate approximation;
Conference_Titel :
Information Reuse & Integration, 2009. IRI '09. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-4114-3
Electronic_ISBN :
978-1-4244-4116-7
DOI :
10.1109/IRI.2009.5211584
