Dynamic time-warping is a method of matching by computing similarity using the elastic distance between two time series. This method was developed for speech recognition and it has been applied to many areas.
This matching method makes one time series to be compressed and stretched nonlinearly in the time axis direction based on the dynamic programming to minimize the elastic distance between two time series of different lengths.
In the literature, matching is implemented by changing the scale of time axis in the time series.
Jong Sung Il, a lecturer at the Faculty of Applied Mathematics, has proposed a time-phase scale varying DTW method of implementing matching by varying the scale of phase as well as that of time axis. Then, he applied it to a sine function with varying amplitude to verify its effectiveness.
For further details, please refer to his paper “Pattern Detection Method using Phase Varying Dynamic Time Warping in Time Series” in “Proceedings of KUTIC-2025”.