A reproducing kernel Hilbert space framework for pairwise time series distances

A good distance measure for time series needs to properly incorporate the temporal structure, and should be applicable to sequences with unequal lengths. In this paper, we propose a distance measure as a principled solution to the two requirements. Unlike the conventional feature vector representation, our approach represents each time series with a summarizing smooth curve in a reproducing kernel Hilbert space (RKHS), and therefore translate the distance between time series into distances between curves. Moreover we propose to learn the kernel of this RKHS from a population of time series with discrete observations using Gaussian process-based non-parametric mixed-effect models. Experiments on two vastly different real-world problems show that the proposed distance measure leads to improved classification accuracy over the conventional distance measures.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Zhengdong Lu, Todd K. Leen, Yonghong Huang, Deniz Erdogmus
• Keywords: Conventional Distance Measures, ICML 2008, Kernel Hilbert Space, Machine Learning, Time Series