Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
<|reference_start|>Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data: 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.<|reference_end|>
@article{dohi2024domain-independent,
    title={Domain-Independent Automatic Generation of Descriptive Texts for
  Time-Series Data},
    author={Kota Dohi, Aoi Ito, Harsh Purohit, Tomoya Nishida, Takashi Endo, Yohei
  Kawaguchi},
    journal={arXiv preprint arXiv:2409.16647},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16647},
    primaryClass={cs.CL cs.LG}
}