Inverse Signal Classification for Financial Instruments
Machine Learning
2013-05-14 v2 Information Retrieval
Statistical Finance
Machine Learning
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.
Keywords
Cite
@article{arxiv.1303.0283,
title = {Inverse Signal Classification for Financial Instruments},
author = {Uri Kartoun},
journal= {arXiv preprint arXiv:1303.0283},
year = {2013}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1303.0073