TY - GEN
T1 - Matching and retrieving sequential patterns under regression
AU - Lei, Hansheng
AU - Govindaraju, Venu
PY - 2004
Y1 - 2004
N2 - Sequential pattern matching and retrieving is of real value. For example, finding stocks in the NASDAQ market whose closing prices are always about $ β 0 higher than or β 1 times as that of a given company. The problem reduces to linear pattern retrieval: given query X, find all sequence Y from database S so that Y = β 0 + β 1X with confidence C. In this paper, we novelty introduce SLR (Simple Linear Regression) model [5, 7] to solve this problem. We extend 1-dimensional R 2 to ER 2 for multi-dimensional sequence matching, such as on-line handwritten signature. In addition, we develop SLR+FFT pruning techniques based on SLR to speed up retrieval without incurring any false dismissal. Experimental results show that the pruning ratio of SLR+FFT is efficient (can be above 99%). Experiments on real stocks discovered many interesting patterns. Preliminary test on on-line signature recognition using ER 2 as similarity measure also shows high accuracy.
AB - Sequential pattern matching and retrieving is of real value. For example, finding stocks in the NASDAQ market whose closing prices are always about $ β 0 higher than or β 1 times as that of a given company. The problem reduces to linear pattern retrieval: given query X, find all sequence Y from database S so that Y = β 0 + β 1X with confidence C. In this paper, we novelty introduce SLR (Simple Linear Regression) model [5, 7] to solve this problem. We extend 1-dimensional R 2 to ER 2 for multi-dimensional sequence matching, such as on-line handwritten signature. In addition, we develop SLR+FFT pruning techniques based on SLR to speed up retrieval without incurring any false dismissal. Experimental results show that the pruning ratio of SLR+FFT is efficient (can be above 99%). Experiments on real stocks discovered many interesting patterns. Preliminary test on on-line signature recognition using ER 2 as similarity measure also shows high accuracy.
UR - https://www.scopus.com/pages/publications/15544377446
M3 - Conference contribution
AN - SCOPUS:15544377446
SN - 0769521002
SN - 9780769521008
T3 - Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
SP - 84
EP - 90
BT - Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
A2 - Zhong, N.
A2 - Tirri, H.
A2 - Yao, Y.
A2 - Zhou, L.
T2 - Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
Y2 - 20 September 2004 through 24 September 2004
ER -