mlsanityData to 14 Oct 2026

Towards Time Series Generation Conditioned on Unstructured Natural Language

arXiv 2506.22927 ↗First posted 28 Jun 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 28 Jun 2025.

Builds on

19 of 38 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2020Denoising Diffusion Implicit Modelscited by 9,781
  6. 2022Generative Adversarial Networkscited by 5,627
  7. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  8. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  9. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
  10. 2021Generative Spoken Language Modeling from Raw Audiocited by 340
  11. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  12. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 142
7 more
  1. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 108
  2. 2020ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Datacited by 43
  3. 2019GRATIS: GeneRAting TIme Series with diverse and controllable characteristicscited by 42
  4. 2024Can LLMs Serve As Time Series Anomaly Detectors?cited by 22
  5. 2024Time-Causal VAE: Robust Financial Time Series Generatorcited by 8
  6. 2024SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learningcited by 6
  7. 2019Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learningcited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach
  2. 2026CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting
  3. 2024AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
  4. 2025T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  6. 2024A Survey of Transformer Enabled Time Series Synthesis
  7. 2024TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation
  8. 2026TS-RAG: Retrieval Augmented Generation for Time Series Forecasting