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

TL;DR

This paper introduces a novel method for automatically generating descriptive texts for time-series data across domains, utilizing a new dataset and contrastive learning to improve generalization.

Contribution

It presents a domain-independent approach and the TACO dataset, enabling models to generate descriptive texts for time-series data in unseen domains.

Findings

Contrastive learning model effectively generates descriptions in new domains

The TACO dataset supports domain-independent text generation

Backward approach improves pairing of data and descriptions

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.