基于I-GARCH的不确定时间序列概率分布推算

Probability Distribution Estimation of Uncertain Time Series Based on I-GARCH

处理不确定数据存储问题的常用方法是使用概率数据库的方法,但是已有的概率数据库生成方法是针对已知概率分布的数据集。不确定时间序列在每一时刻的概率分布规律随着时间的变化而变化,无法使用统一的概率推导方法进行计算,因此已有的概率数据库生成方法不再适用。为解决该问题,依托已有的ARMA模型和GARCH模型,提出推导不确定时间序列概率分布的推算模型。同时,为了进一步增强该模型的容错性,提出了相应的错误值过滤算法。实验结果表明,该模型能够有效地根据不确定时间序列的发展规律,动态地进行调整计算,得出不确定时间序列的概率分布;同时,容错算法能够很好地探测找到数据集中的错误数据,进行数据的清洗与替换,体现出良好的容错性与一般通用性。

One of the most effective ways to deal with uncertain time series is to employ probabilistic database.But existing methods of generating probabilistic database are typically based on the assumption that the probabilistic distribution is already known.The law of probability distributions of uncertain time series varies with time and cannot be calculated by a uniform method,so the existing methods of generating probabilistic database are no longer applicable.To solve the problem,based on the existing ARMA model and GARCH model,we propose a prediction model for deriving the probability distribution of uncertain time series.In addition,in order to enhance the fault tolerance of the model,we propose a corresponding erroneous value filtering algorithm.Experiment shows that the model can effectively adjust and calculate the probability distribution of uncertain time series,which is in line with the development law of the origin uncertain time series.Furthermore,the erroneous value filtering algorithm can detect and find the erroneous values well,and then wash and replace the data with inferred correct values,which shows great fault tolerance and commonality.