A novel approach for mining multiple data streams based on lag correlation

Correlation analysis is a key problem for data stream analysis. In this paper, we propose a correlation analysis method for multiple dimensional data streams, which is based on the Boolean lag representation and the PCA (Principal Component Analysis). Firstly, the raw stream sequence is transformed into the Boolean sequence. By the correlation analysis of Boolean sequences, we can easily find the sequence pairs with lag correlations by means of simple bit operations. Secondly, we compute the lag time and synchronize the multiple dimensional data stream. Thirdly, the PCA method is deployed to reduce the multiple data streams, and we can reconstruct the data streams by a few principal components. The experimental evaluations show that the method has high computation performance with high accuracy.