Similarity Matching for Uncertain Time Series
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摘要: 确定性时间序列的相似性匹配方法都没有考虑数据的不确定性，而现实世界中传感器采集到的数据往往是不确定的，现有的时间序列的相似性匹配方法不适用于这些领域.针对此问题，将不确定性时间序列做预处理，把它分为横向时间维和纵向概率维，首先把给定的不确定时间序列用Haar小波变换进行压缩变换，在此基础上，对得到的不确定性时间序列概率维作纵向处理，提出一种选代表方法，即采用概率最大法、均值法等选出一条确定的时间序列.通过这2种预处理后，对得到的确定性时间序列进行降维和索引，根据查询序列和数据库中的时间序列中的各自的不确定性进行组合，分别提出对应组合的相似性匹配算法.Abstract: Similarity matching techniques for certain time series do not consider the uncertainty of data, but in the real world the time series data collected by the sensors is often not certain, To solve this problem, we perform pre-processing over uncertain time series. It is divided into horizontal and vertical dimensions, that is, time dimension and probability dimension. First, an uncertain time series is compressed by the Haar wavelet transform. On this basis, we process the obtained uncertain time series longitudinally, and put forward a kind of method of electing representatives, which adopts maximum probability method and the mean method to select a certain time sequence. After pretreatment, we carry on the dimensionality reduction and indexing with generated certain time series. According to the query sequence and each time series in the database in the combination of uncertainty, we put forward the similarity matching algorithm corresponding to a combination of them respectively.
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Keywords:
- time series /
- uncertainty /
- matching /
- dimensionality reduction /
- Haar wavelet transform
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