2013/05/12 by Uri Kartoun, Kartoun, Uri
Computer Science · Engineering · Medicine · #Heart Rate Variability and Autonomic Control #Non-Invasive Vital Sign Monitoring #Time Series Analysis and Forecasting #cs.CE #cs.LG
paper · pdf · doi:10.48550/arxiv.1306.0541
arXiv admin note: text overlap with arXiv:1303.0073
arxiv created 2013/05/12 · arxiv updated 2013/06/04
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.