title: Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data.
abstract: Finding similar sequences in time series has received much attention and is a widely studied topic. Most existing approaches in the time series area focus on the efficiency of algorithms but seldom provide a means to handle imprecise data. In this paper, a more general approach is proposed to measure the distance of time sequences containing crisp values, intervals, and fuzzy intervals as well. The concept of distance measurement and its associated dynamic-programming-based algorithms are described. In addition to finding the sequences with similar evolving trends, a means of finding the sequences with opposite evolving tendencies is also proposed, which is usually omitted in current related research but could be of great interest to many users.
authors.affiliation: Department of Information Systems, City University of Hong Kong, Hong Kong. issliao@cityu.edu.hk
authors.forename: Stephen Shaoyi
authors.initials: SS
authors.lastname: Liao
authors.forename: Tony Heng
authors.initials: TH
authors.lastname: Tang
authors.forename: Wei-Yi
authors.initials: WY
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017143:Fuzzy Logic; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted