  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.
