Inverse Signal Classification for Financial Instruments
AbstractThe paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system using a collection of 7,881 financial instruments traded during 2011 to identify inverse behavior among the time-series.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1303.0283.
Date of creation: Feb 2013
Date of revision: Mar 2013
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-03-09 (All new papers)
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