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

arXiv: https://arxiv.org/abs/2409.16647 · HTML: https://mlsanity.com/paper/2409.16647/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [Ask Me Anything: A simple strategy for prompting language models](https://mlsanity.com/paper/2210.02441/) (2022)
- [SECap: Speech Emotion Captioning with Large Language Model](https://mlsanity.com/paper/2312.10381/) (2023)
- [LineCap: Line Charts for Data Visualization Captioning Models](https://mlsanity.com/paper/2207.07243/) (2022)
- [Truth-Conditional Captioning of Time Series Data](https://mlsanity.com/paper/2110.01839/) (2021)

## Built on by (most cited first, 6 of 6)

- [How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook](https://mlsanity.com/paper/2503.11835/) (2025)
- [CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision](https://mlsanity.com/paper/2411.08397/) (2024)
- [Retrieving Time-Series Differences Using Natural Language Queries](https://mlsanity.com/paper/2503.21378/) (2025)
- [DiffNator: Generating Structured Explanations of Time-Series Differences](https://mlsanity.com/paper/2509.20007/) (2025)
- [Can VLM Pseudo-Labels Train a Time-Series QA Model That Outperforms the VLM?](https://mlsanity.com/paper/2509.25696/) (2025)
- [Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions](https://mlsanity.com/paper/2603.23375/) (2026)

## Abstract (arXiv)

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.