Towards Time Series Generation Conditioned on Unstructured Natural Language
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913917231431680 |
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| author | Woo, Jaeyun Lee, Jiseok Iwana, Brian Kenji |
| author_facet | Woo, Jaeyun Lee, Jiseok Iwana, Brian Kenji |
| contents | Generative Artificial Intelligence (AI) has rapidly become a powerful tool, capable of generating various types of data, such as images and text. However, despite the significant advancement of generative AI, time series generative AI remains underdeveloped, even though the application of time series is essential in finance, climate, and numerous fields. In this research, we propose a novel method of generating time series conditioned on unstructured natural language descriptions. We use a diffusion model combined with a language model to generate time series from the text. Through the proposed method, we demonstrate that time series generation based on natural language is possible. The proposed method can provide various applications such as custom forecasting, time series manipulation, data augmentation, and transfer learning. Furthermore, we construct and propose a new public dataset for time series generation, consisting of 63,010 time series-description pairs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Time Series Generation Conditioned on Unstructured Natural Language Woo, Jaeyun Lee, Jiseok Iwana, Brian Kenji Machine Learning Generative Artificial Intelligence (AI) has rapidly become a powerful tool, capable of generating various types of data, such as images and text. However, despite the significant advancement of generative AI, time series generative AI remains underdeveloped, even though the application of time series is essential in finance, climate, and numerous fields. In this research, we propose a novel method of generating time series conditioned on unstructured natural language descriptions. We use a diffusion model combined with a language model to generate time series from the text. Through the proposed method, we demonstrate that time series generation based on natural language is possible. The proposed method can provide various applications such as custom forecasting, time series manipulation, data augmentation, and transfer learning. Furthermore, we construct and propose a new public dataset for time series generation, consisting of 63,010 time series-description pairs. |
| title | Towards Time Series Generation Conditioned on Unstructured Natural Language |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.22927 |