Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
This work addresses the problem of data scarcity for training text-generation models in time-series analysis, though it appears incremental as it builds on existing contrastive learning techniques.
The authors tackled the challenge of generating descriptive texts for time-series data by proposing a domain-independent method, creating the TACO dataset using a novel backward approach, and achieving the ability to generate texts in novel domains with a contrastive learning model.
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.