  The paper presents a time-series-based classification approach to identify
similarities in pairs of simulated human-generated patterns. An example for a
pattern is a time-series representing a heart rate during a specific
time-range, wherein the time-series is a sequence of data points that represent
the changes in the heart rate values. A bio-medical simulator system was
developed to acquire a collection of 7,871 price patterns of financial
instruments. The financial instruments traded in real-time on three American
stock exchanges, NASDAQ, NYSE, and AMEX, simulate bio-medical measurements. The
system simulates a human in which each price pattern represents one bio-medical
sensor. Data provided during trading hours from the stock exchanges allowed
real-time classification. Classification is based on new machine learning
techniques: self-labeling, which allows the application of supervised learning
methods on unlabeled time-series and similarity ranking, which applied on a
decision tree learning algorithm to classify time-series regardless of type and
quantity.
