Finding relevant sequences in time series containing crisp, interval, and fuzzy interval data.
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.
Liao
Stephen Shaoyi
SS
Department of Information Systems, City University of Hong Kong, Hong Kong. issliao@cityu.edu.hk
Tang
Tony Heng
TH
Liu
Wei-Yi
WY
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