A Novel RDB-SW Approach for Commodities Price Dynamic Trend Analysis Based on Web Mining
Quanyin Zhu, Pei Zhou, Suqun Cao, Yunyang Yan, Jin Ding
Journal of Digital Information Management, Vol. 10, No. 4, 2012.
DOI: 
Link: http://dline.info/fpaper/jdim/v10i4/2.pdf
Abstract:
In order to improve the accuracy of price forecasting for dynamic trend analysis on imperfect data by web extracting, a novel repair data algorithm based on dichotomy backfilling is proposed in this paper. The price forecasting algorithm based on the Sliding Window (SW) is utilized to verify the validity of Repair Dichotomy Backfilling (RDB) algorithm. Experiments demonstrated that the mean absolute errors can be achieved to 5.31 percent average. Furthermore, the price forecasting algorithm based on original data and that based on RDB data are explored to compare their accuracy. Experiments demonstrated the reduction of mean absolute errors in the price dynamic trend analysis verification model. The average MAE on original data is 2.38 percent when the average MAE on RDB data is 2.31 percent. Experiment results proved that the proposed RDB-SW approach is meaningful and useful to analyze and to research the price market on imperfect data by Web extracting.