Inverse Signal Classification for Financial Instruments
This addresses the challenge of analyzing diverse financial time-series data for investors or analysts, but appears incremental as it builds on existing supervised-learning enhancements.
The paper tackled the problem of classifying time-series of varying lengths, types, and quantities in finance by introducing signal composition and self-labeling methods, applied to 7,881 financial instruments from 2011 to identify inverse behavior.
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.