模拟生物医学时间序列中的配对识别
Identifying Pairs in Simulated Bio-Medical Time-Series
摘要：
本文提出了一种基于时间序列的分类方法，以识别相似性对模拟人类产生的模式。模式的示例是表示在特定时间范围期间的心率的时间序列，其中时间序列是表示心率值的变化的数据点的序列。开发了一个生物医学模拟器系统，以收集7 871种金融工具的价格模式。这些金融工具在三个美国证券交易所（纳斯达克、纽约证券交易所和美国证券交易所）进行实时交易，模拟生物医学测量。该系统模拟一个人，其中每个价格模式代表一个生物医学传感器。证券交易所在交易时间内提供的数据可以进行实时分类。分类是基于新的机器学习技术：自标记，它允许应用监督学习方法对未标记的时间序列和相似性排序，它应用于决策树学习算法分类时间序列，而不管类型和数量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
