%0 Journal Article
%A HUA Qing
%A XU Guoyan
%A ZHANG Ye
%T Improved Kalman algorithm for abnormal data detection based on multidimensional impact factors
%D
%R 10.11772/j.issn.1001-9081.2015.11.3112
%J Journal of Computer Applications
%P 3112-3115
%V 35
%N 11
%X With the widespread application of the data flow, the abnormal data detection problem in data flow has caused more attention. Existing Kalman filtering algorithms need small amount of historical data, but they only apply to single abnormal point detection. The effect to complex continuous outlier points is poor. In order to solve the problem, a Kalman filtering algorithm based on multidimensional impact factors was proposed. The algorithm joined the three dimensions of impact factor as space, time, provenance as well. In case of different weather and flood season, the algorithm adjusted the controlling parameters of system model parameters, and got a more accurate estimate of measurement noise. The detection accuracy of the algorithm could be improved significantly. The experimental results show that under the premise of guaranteeing similar running time, the detection error rate of this algorithm is far lower than Amnesic Kalman Filtering (AKF) and Wavelet Kalman Filtering (WKF) algorithms.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2015.11.3112