摘要
将平均移位柱状图(averaged shifted histogram,ASH)概率密度估计中二次型平滑权值与均匀权值进行结合,提出一种改进的概率密度估计方法:IASH(improved averaged shifted histogram).通过相应区间内样本数目的方差确定原平滑权值与均匀权值之间的比例系数,动态的改变平滑权值:对ASH概率密度估计中边缘值部分的平滑权值按比例进行补偿,改善过平滑的问题,提高了IASH概率密度估计的精度.在此基础上应用互信息进行变量间的相关性分析,选择输入变量,实现多元时间序列的预测.采用人工数据和实际Housing数据进行仿真分析,仿真结果验证了改进后方法的有效性.
We introduce the method of improved averaged-shifted-histogram（lASH） to estimate the probability density by combining the quadratic smooth weight with the uniform smooth weight. The ratio of the original smooth weight to the uniform smooth weight is dynamically adjusted according to the variance of the number of samples in the corresponding interval, thus the smooth weight for the edge part of the probability density obtained by the method of averaged-shifted- histogram（ASH） is proportionally compensated, mitigating the excessive smoothness and improving the precision in the estimation of probability density by the method of lASH. Using the estimated probability density, we perform the correla- tion analysis based on the mutual information between two variables, and select input variables to predict the multivariate time series. Simulations with the synthetic data and Housing data show the efficacy of the proposed method.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2011年第6期845-850,共6页
Control Theory & Applications
基金
国家自然科学基金资助项目(60674073)
国家科技支撑计划资助项目(2006BAB14B05)
国家高技术研究发展"863"计划资助项目(2007AA04Z158)
关键词
平均移位柱状图
互信息
相关性分析
时间序列预测
averaged-shifted-histogram
mutual information
correlation analysis
time series prediction
