Inverse Signal Classification for Financial Instruments

Autor:	Kartoun, Uri
Rok vydání:	2013
Předmět:	Computer Science - Learning Computer Science - Information Retrieval Quantitative Finance - Statistical Finance Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: substantial text overlap with arXiv:1303.0073
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1303.0283 Zobrazit plný text záznamu View this record from Arxiv