Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
Kota Dohi, Aoi Ito, Harsh Purohit, Tomoya Nishida, Takashi Endo, Yohei Kawaguchi
Abstract
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.
BibTeX
@inproceedings{icassp2025_domainindependen,
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},
booktitle = {ICASSP 2025},
year = {2025}
}