Real-time algorithm for trend analysis of dynamic data streams

Efficient trend extraction methods can provide early warnings, severity assessments of monitored subjects and information for decision support. In this paper, a real-time trend extraction algorithm for dynamic data streams algorithm is proposed by combining an incremental recursive least square algorithm for regression parameter estimation with the generalized likelihood ratio test for change-point detection. Remarkably faster computational speed and higher trend analysis accuracy are achieved by this algorithm compared with the existing algorithms in the same field. The experiment results show the effectiveness of the proposed algorithm.