Data Stream Prediction Model Based on Parameters Smoothing Methods
Long Ha · Journal of Hebei Software Institute · 2014
Fixed window width model means the size of the sliding window width is a fixed number. Based on this model,this paper proposed a data stream prediction model based on regression parameters smoothing method. Compared with the sliding window model,a new buffer zone is set for the storage of the parameters which calculated by recent data. Then a new data stream prediction algorithm is proposed. By using the data in current data buffer to get the prediction function,we can get the prediction value in current window. Given a fixed deviation value,if the difference between the prediction results and observation data not less than the deviation,parameters correction step need to be done,in which the prediction function parameters need to be changed. Experiments show us that this algorithm can reduce the space and time complexity,and also improves the prediction accuracy.