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

• URL: http://arxiv.org/abs/2409.16647v1
• Date: Wed, 25 Sep 2024 06:04:03 GMT
• Title: Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
• Authors: Kota Dohi, Aoi Ito, Harsh Purohit, Tomoya Nishida, Takashi Endo, Yohei Kawaguchi,
• Abstract summary: We propose a method to generate domain-independent descriptive texts from time-series data. By implementing the novel backward approach, we create the Temporal Automated Captions for Observations 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.
• Score: 5.264562311559751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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