Generation of Variable-Length Time Series from Text using Dynamic Time Warping-Based Method

This study is aimed at finding a suitable method for generating time-series data such as video clips or avatar motions from text stating multiple events. This paper addresses the generation of variable-length time-series data considering the order and variable duration of events stated in the text. Although the use of the variant of Mean Squared Error (MSE) is a common means of training, only the gap between the element of ground-truth (GT) data and generated data at the same time are considered. Thus, variants of MSE are unsuitable for the task at hand because the loss may not be small for the generated and GT data with the same order of events if the time for each event does not overlap. To solve the problem, we propose a Dynamic Time Warping-Like method for Variable-Length data (DTWL-VL), which determines the corresponding elements of the GT and the generated data, allowing for the time difference between them, and makes them closer. We compared DTWL-VL, a variant of MSE, and an existing method for time-series data generation which considers the time difference between the corresponding part in the GT and generated data. Since the existing method is aimed at generating fixed-length data, we extend the method for generating variable-length time-series data. We conducted experiments using a dataset prepared for this study. Both DTWL-VL and the existing methods outperformed the MSE variant. Moreover, although the existing method outperformed DTWL-VL under certain settings, DTWL-VL required a smaller training period.