Similarity Matching for Uncertain Time Series

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.