计算机科学与探索Issue(4)：418-428,11.DOI:10.3778/j.issn.1673-9418.1407039
不确定时间序列的降维及相似性匹配
Dimensionality Reduction and Similarity Match of Uncertain Time Series
摘要
Abstract
The value of uncertain time series at each timeslot is derived from a set with possible values, it is hard to judge which one is the determined value. This uncertainty is a huge challenge for dimensionality reduction and simi-larity match. Existing time series dimensionality reduction and similarity match methods have been unable to apply. To solve this problem, this paper models uncertain time series with descriptive statistics, reduces an uncertain time series to three certain time series which dimensionality is reduced by DFT (discrete Fourier transform), DCT (discrete cosine transform) and DWT (discrete wavelet transform). This paper also presents the similarity match algorithm based on observations interval and central tendency. After the trial validation, under the descriptive statistics model, DCT and DWT perform well in dimensionality reduction, the similarity match algorithm proposed in this paper is superior to others existed.关键词
不确定时间序列/降维/相似性匹配/离散傅里叶变换(DFT)/离散余弦变换(DCT)/离散小波变换(DWT)Key words
uncertain time series/dimensionality reduction/similarity match/discrete Fourier transform (DFT)/dis-crete cosine transform (DCT)/discrete wavelet transform (DWT)分类
信息技术与安全科学引用本文复制引用
王伟,刘国华,徐斌..不确定时间序列的降维及相似性匹配[J].计算机科学与探索,2015,(4):418-428,11.基金项目
The National Natural Science Foundation of China under Grant No.61070032(国家自然科学基金) （国家自然科学基金）
