Piecewise factorization for time series classification

Abstract

In the research field of time series analysis and mining, the nearest neighbor classifier (1NN) based on the dynamic time warping distance (DTW) is well known for its high accuracy. However, the high computational complexity of DTW can lead to the expensive time consumption of the classifier. An effective solution is to compute DTW in the piecewise approximation space (PA-DTW). However, most of the existing piecewise approximation methods must predefine the segment length and focus on the simple statistical features, which would influence the precision of PA-DTW. To address this problem, we propose a novel piecewise factorization model (PCHA) for time series, where an adaptive segment method is proposed and the Chebyshev coefficients of subsequences are extracted as features. Based on PCHA, the corresponding PA-DTW measure named ChebyDTW is proposed for the 1NN classifier, which can capture the fluctuation information of time series for the similarity measure. The comprehensive experimental evaluation shows that ChebyDTW can support both accurate and fast 1NN classification.