TY - GEN
T1 - Regression time warping for similarity measure of sequence
AU - Lei, Hansheng
AU - Govindaraju, Venu
PY - 2004
Y1 - 2004
N2 - In the paper, we propose Regression Time Warping (RTW), a novel similarity measure for sequence or time series matching. RTW fuses the Linear Regression analysis, which controls the shifting and scaling factors between sequences [3], and the principles of Dynamic Time Warping (DTW), which provides robustness with elastic matching. RTW has complexity as low as O(n),while the complexity of DTW is O(n 2). Experimental results show the accuracy of RTW in classification is comparable to DTW, and much faster than DTW in term of running time.
AB - In the paper, we propose Regression Time Warping (RTW), a novel similarity measure for sequence or time series matching. RTW fuses the Linear Regression analysis, which controls the shifting and scaling factors between sequences [3], and the principles of Dynamic Time Warping (DTW), which provides robustness with elastic matching. RTW has complexity as low as O(n),while the complexity of DTW is O(n 2). Experimental results show the accuracy of RTW in classification is comparable to DTW, and much faster than DTW in term of running time.
UR - https://www.scopus.com/pages/publications/9744271790
M3 - Conference contribution
AN - SCOPUS:9744271790
SN - 0769522165
SN - 9780769522166
T3 - Proceedings - The Fourth International Conference on Computer and Information Technology (CIT 2004)
SP - 826
EP - 830
BT - Proceedings - The Fourth International Conference on Computer and Information Technology (CIT 2004)
A2 - Wei, D.
A2 - Wang, H.
A2 - Peng, Z.
A2 - Kara, A.
A2 - He, Y.
T2 - Proceedings - The Fourth International Conference on Computer and Information Technology (CIT 2004)
Y2 - 14 September 2004 through 16 September 2004
ER -