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arXiv · 2409.16647

Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data

Kota Dohi·Aoi Ito·Harsh Purohit·Tomoya Nishida·Takashi Endo·Yohei Kawaguchi
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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.

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Kota Dohi, Aoi Ito, Harsh Purohit, Tomoya Nishida, Takashi Endo, Yohei Kawaguchi. 2024-09-25. Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data. https://arxiv.org/abs/2409.16647

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