基于随机性分析的虚假趋势时间序列判别

针对符合一定数据模式或规律的虚假数据识别问题,提出一种基于随机性分析的虚假趋势时间序列判别方法。该方法在分析时间序列组成的基础上,首先探索虚假趋势时间序列的简单伪造和复杂伪造方式,并将其分解为虚假趋势和虚假随机两部分;然后通过基函数逼近进行时间序列虚假趋势部分的提取,采用随机性理论开展虚假随机部分的分析;最终借助单比特频数和块内频数对虚假随机部分是否具备随机性进行检测,为具有一定趋势特征的虚假时间序列的判别提供了一个解决方案。实验结果表明:该方法能够有效地分解虚假时间序列和提取虚假趋势部分,实现简单伪造数据和复杂伪造数据的判别,支持对通过观测手段或者检测设备所获取的数值型数据的真伪分析,进一步提高了虚假数据可判别范围,平均判别正确率可达74.7%。

False trend time series detection based on randomness analysis

Focusing on the detection problem of false data that conform to a certain pattern or rule, a false trend time series detection method based on randomness analysis was proposed. Based on the analysis of time series composition, firstly the simple forgery method and complex forgery method of false trend time series were explored, and decomposed into two parts:false trendness and false randomness. Then the false trend of time series was extracted by the approximation of base function, the false random of time series was analyzed with the randomness theory. Finally, monobit frequency and frequency within a block were adopted to test whether the false random part has randomness, which established a detection method of false time series with a certain trend feature. The simulation results show that proposed method can decompose the false time series and extract the false trend part effectively, meanwhile realize the detection of simple and complex forged data. It also supports the authenticity analysis for the numerical data obtained by means of observation or monitoring equipment, which improves the discrimination range of false data with average detection accuracy of 74.7%.