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

fYear

2009

fDate

10-12 Aug. 2009

Firstpage

388

Lastpage

393

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IRI.2009.5211584

Filename

5211584