Due to scarcity of time-series data annotated with descriptive texts, training a model to generate descriptive texts for time-series data is challenging. In this study, we propose a method to systematically generate domain-independent descriptive texts from time-series data. We identify two distinct approaches for creating pairs of time-series data and descriptive texts: the forward approach and the backward approach. By implementing the novel backward approach, we create the Temporal Automated Captions for Observations (TACO) dataset. Experimental results demonstrate that a contrastive learning based model trained using the TACO dataset is capable of generating descriptive texts for time-series data in novel domains.
@article{arxiv.2409.16647,
title = {Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data},
author = {Kota Dohi and Aoi Ito and Harsh Purohit and Tomoya Nishida and Takashi Endo and Yohei Kawaguchi},
journal= {arXiv preprint arXiv:2409.16647},
year = {2025}
}