Estimating Dynamic Time Warping Distance Between Time Series with Missing Data KU Leuven
Many techniques for analyzing time series rely on some notion of similarity between two time series, such as Dynamic Time Warping (DTW) distance. But DTW cannot handle missing values, and simple fixes (e.g., dropping missing values, or interpolating) fail when entire intervals are missing, as is often the case with, e.g., temporary sensor or communication failures. There is hardly any research on how to address this problem. In this paper, we ...