Correlation Analysis Based on Hierarchical Boolean Representation over Time Series Data Streams

Abstract

Correlation analysis is a basic problem in the field of data stream mining. Traditional method is not suitable for real time processing with huge amount of stream data. We propose a hierarchical Boolean representation method for correlation analysis among time series data streams. The original streaming series are transformed into the Macro- Boolean series and then the Micro-Boolean series successively, and the candidate can be easily gained by simple bit operations. With huge amount of streaming series, this method can quickly get the correlation pairs of series in an efficient way by reducing huge calculation in a little space The experimental evaluations show that our method has better computation complexity with high accuracy.