中文摘要：

文章为在非平稳时间序列的在线学习理论的基础上检测离群点和变化点提出了一个统一框架.在这个框架中数据源的一个概率模型用一种在线折扣学习算法被逐步学习,该算法能通过逐渐忘记过去数据的效果自适应地跟踪变化的数据源.然后任一给定数据的分数被计算出来测量它与学习模型的偏差,高分表明更有可能是离群点.进一步地数据流中的变化点通过用这一学习模型应用这种得分方法到一个移动平均损失预测时间序列中来检测.特别地我们为来自时间序列数据的自回归模型的在线折扣学习发明了一种有效算法,并通过仿真和在股票市场数据分析的实际应用验证框架的有效性.

英文摘要：

We present a unifying framework for dealing with outlier and change point on the basis of the theory of on-line learning of non-stationary time series.In this framework a probabilistic model of the data source is incrementally learned using an on-line discounting learning algorithm,which can track the changing data source adaptively by forgetting the effect of past data gradually.Then the score for any given data is calculated to measure its deviation from the learned model,with a higher score indicating a high possibility of being an outlier.Further change points in a data stream are detected by applying this scoring method into a time series of moving averaged losses for prediction using the learned model.Specifically we develop an efficient algorithms for on-line discounting learning of auto-regression models from time series data,and demonstrate the validity of our framework through simulation and experimental applications to stock market data analysis.