Order, Duration and Gap-Take Them All
Based on a formal characterization of timeseries and state-sequences, a new distance measurement dealing with both non-temporal and temporal distances for state-sequence matching is proposed in this paper. In addition to formulating the temporal order over state-sequences, it also takes into account of temporal distances in terms of both the temporal duration of each state and the temporal gaps between adjacent pairs of states, which are neglected in most existing approaches to timeseries and state-sequence matching. In particular, when specialized as a real-penalty-style measurement by means of reifying the cost functions, it is more flexible with regards to real-life applications than binary-value-style distance measurements. In addition, it is more robust than those existing realpenalty-style distance measurements since it can filter out noise during the matching procedure. Experimental results on reconstructed time-series data from UCI KDD Archive demonstrate that it can tackle the most general problems in matching timeseries data with rich temporal information.
Pattern Recongition Time-series State-sequence Matching
Aihua Zheng Jixin Ma Miltos Petridis BaiXiao
School of Computing and Mathematical Sciences The University of Greenwich, London, UK Department of Computer Science Beihang University, Beijing.China
国际会议
成都
英文
647-651
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）