Adaptive correlation analysis in stream time series with sliding windows

Correlation analysis is a very useful technique for similarity search in the field of data stream mining. The traditional method is not suitable for real time processing especially when the amount of stream sequences is very large. In this paper, we propose HBR (Hierarchical Boolean Representation), a novel technique for correlation analysis in stream time series. The original stream sequences are transformed into the Macro-Boolean series and the Micro-Boolean series successively, and the candidate correlation set can be easily obtained by simple bit operations. With huge amount of stream series, this method can quickly get the correlation pairs of series efficiently by reducing complicated calculation in a little space. Meanwhile, this approach can update the Boolean series incrementally with very low cost and adjust some important coefficients adaptively by the stream feature. The experimental evaluations show that HBR has excellent computation complexity with high accuracy.