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Inverse Signal Classification for Financial Instruments

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  • Uri Kartoun

Abstract

The 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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  • Uri Kartoun, 2013. "Inverse Signal Classification for Financial Instruments," Papers 1303.0283, arXiv.org, revised Mar 2013.
    • Handle: RePEc:arx:papers:1303.0283
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