Dimensionality Reduction and Similarity Match of Uncertain Time Series
Dan Wang · Jisuanji kexue yu tansuo · 2015
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 similarity 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.