Curves Clustering Based on Quantile Regression

摘要：

The motivation of time series classification is to find similar volatility structures, reduce the workload and forecast, hence the outcome of classification would directly impact on the quality of models and the accuracy of forecasts. For this purpose, the paper proposed a new method of time series classification QRP Clustering. QRP Clustering can avoid some limitations brought by several classification methods, fully test the operation of time series waiting to be classified, improve the effectiveness of classification and provide strong support for forecasts.

关键词： Quantile Regression Common Variable Hierarchical Cluster Integrated Forecast

作者: Sun Xiaodan

作者单位: School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

会议类型: 国际会议

会议名称: The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)

会议地点: 北京

会议语种:英文

页码: 404-409

在线出版日期: 2009-09-04（万方平台首次上网日期，不代表论文的发表时间）