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Gene Trajectory Clustering for Learning the Stock Market Sectors

Domenii publicaţii > Ştiinţe informatice + Tipuri publicaţii > Capitol de carte

Autori: Darie Moldovan and Gheorghe Cosmin Silaghi

Editorial: Springer, Adaptive and Natural Computing Algorithms, Lecture Notes in Computer Science, 5495, p.559-568, 2009.

Rezumat:

Hybrid Gene Trajectory Clustering (GTC) algorithm [1,2] proves to be a good candidate to cluster multi-dimensional noisy time series. In this paper we apply the hybrid GTC to learn the structure of the stock market and to infer interesting relationships out of closing prices data. We conclude that hybrid GTC can successfully identify homogeneous and stable stock clusters and these clusters can further help the investors.

Cuvinte cheie: gene trajectory clustering, expectation maximization, stock market sectors

URL: http://www.springerlink.com/content/e3n10634jl4x05m3/?p=21a445ff0cac4502a073c34dad688352&pi=3