The Application of Elastic Distance in Astrophysical Time Series
Abstract
Elastic distances, e.g. dynamic time warping (DTW), evaluate the similarity between query and reference sequences by dynamic programming. The 1-Nearest-Neighbor predictor with DTW is one benchmark in time series classification. However, DTW is less efficient in astronomical time series because of ignorance of the information in time stamps and its dependence on the shape and magnitude between query and reference sequences. We apply two elastic distances which integrate the information in the time domain, time warp editing distance (TWED) and Skorohod distance, which is calculated by using Fre ́chet distance, to three astronomical datasets to compare with DTW and Euclidean distance. The first dataset aims to classify signals emitted from various astronomical sources with multiple bandpasses from the Large Synoptic Survey Telescope (LSST). The TWED shows the optimal 1-NN classification performance with a 0.74 loose accuracy. In the second dataset, we explore the possibility of shrinking the size of the gravitational wave (GW) template banks to reduce the computational waste of matching data with similar templates. With the threshold of 5%, similar templates can be removed without losing the effectualness of the template bank. In the final dataset from LIGO and Virgo detections, we establish an early warning process of GW by locating the coincident period between detectors. Though DTW distance outperforms others, TWED achieves a 0.72 detection ratio and a 0.47 average significant ratio. These results of three astrophysical datasets reveal the applicability of elastic distances in the astrophysical domain, especially TWED.