Pseudo-Local Alignment of Continuous Time Series
نویسنده
چکیده
Time series occur throughout nature and within almost every discipline of science. Producing accurate alignments of time series data was made feasible with the Dynamic Time Warping algorithm. Through stretching and compressing of individual points in time series data, this algorithm produces accurate and intuitive global time series alignments. In this paper, we extend the DTW algorithm to perform pseudo-local alignments with the Local Dynamic Time Warping (LDTW) algorithm. We apply the LDTW algorithm to simple time series data to demonstrate its ability to find local areas of high similarity. Finally, we present the use of LDTW as an initial step for extending a recent and more robust HMM times series alignment algorithm to also allow it to perform local
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