Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation

• URL: http://arxiv.org/abs/2505.17103v2
• Date: Mon, 03 Nov 2025 16:31:16 GMT
• Title: Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation
• Authors: Cécile Rousseau, Tobia Boschi, Giandomenico Cornacchia, Dhaval Salwala, Alessandra Pascale, Juan Bernabe Moreno,
• Abstract summary: SDForger is a framework for generating high-quality time series using LLMs.<n>By enabling textual conditioning in the generation process, SDForger paves the way for multimodal modeling.<n>The model is open-sourced at https://github.com/IBM/fms-dgt/tree/main/fms_dgt/public/databuilders/time_series.
• Score: 38.95205270397238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: SDForger is a flexible and efficient framework for generating high-quality multivariate time series using LLMs. Leveraging a compact data representation, SDForger provides synthetic time series generation from a few samples and low-computation fine-tuning of any autoregressive LLM. Specifically, the framework transforms univariate and multivariate signals into tabular embeddings, which are then encoded into text and used to fine-tune the LLM. At inference, new textual embeddings are sampled and decoded into synthetic time series that retain the original data's statistical properties and temporal dynamics. Across a diverse range of datasets, SDForger outperforms existing generative models in many scenarios, both in similarity-based evaluations and downstream forecasting tasks. By enabling textual conditioning in the generation process, SDForger paves the way for multimodal modeling and the streamlined integration of time series with textual information. The model is open-sourced at https://github.com/IBM/fms-dgt/tree/main/fms_dgt/public/databuilders/time_series.

Related papers

• Fusing Large Language Models with Temporal Transformers for Time Series Forecasting [17.549938378193282]
  Large language models (LLMs) have demonstrated powerful capabilities in performing various tasks.<n>LLMs are proficient at reasoning over discrete tokens and semantic patterns.<n> vanilla Transformers often struggle to learn high-level semantic patterns.
  arXiv  Detail & Related papers  (2025-07-14T09:33:40Z)
• Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs [6.612196783595362]
  We propose a multi-level text alignment framework for time series forecasting using large language models (LLMs)<n>Our method decomposes time series into trend, seasonal, and residual components, which are then reprogrammed into component-specific text representations.<n> Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art models in accuracy while providing good interpretability.
  arXiv  Detail & Related papers  (2025-04-10T01:02:37Z)
• Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative [65.84249211767921]
  Texts as Time Series (TaTS) can be plugged into any existing numerical-only time series models.<n>We show that TaTS can enhance predictive performance without modifying model architectures.
  arXiv  Detail & Related papers  (2025-02-13T03:43:27Z)
• Large Language Models are Few-shot Multivariate Time Series Classifiers [23.045734479292356]
  Large Language Models (LLMs) have been extensively applied in time series analysis.<n>Yet, their utility in the few-shot classification (i.e., a crucial training scenario) is underexplored.<n>We aim to leverage the extensive pre-trained knowledge in LLMs to overcome the data scarcity problem.
  arXiv  Detail & Related papers  (2025-01-30T03:59:59Z)
• Time Series Language Model for Descriptive Caption Generation [11.796431549951055]
  We introduce TSLM, a novel time series language model designed specifically for time series captioning.<n>TSLM operates as an encoder-decoder model, leveraging both text prompts and time series data representations.<n>We show that TSLM outperforms existing state-of-the-art approaches from multiple data modalities by a significant margin.
  arXiv  Detail & Related papers  (2025-01-03T14:34:30Z)
• Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification [4.5939667818289385]
  HiTime is a hierarchical multi-modal model that seamlessly integrates temporal information into large language models. Our findings highlight the potential of integrating temporal features into LLMs, paving the way for advanced time series analysis.
  arXiv  Detail & Related papers  (2024-10-24T12:32:19Z)
• TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment [21.690191536424567]
  TimeCMA is an intuitive yet effective framework for time series forecasting.<n>Extensive experiments on eight real datasets demonstrate that TimeCMA outperforms state-of-the-arts.
  arXiv  Detail & Related papers  (2024-06-03T00:27:29Z)
• AutoTimes: Autoregressive Time Series Forecasters via Large Language Models [67.83502953961505]
  AutoTimes projects time series into the embedding space of language tokens and autoregressively generates future predictions with arbitrary lengths. We formulate time series as prompts, extending the context for prediction beyond the lookback window. AutoTimes achieves state-of-the-art with 0.1% trainable parameters and over $5times$ training/inference speedup.
  arXiv  Detail & Related papers  (2024-02-04T06:59:21Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Merlion: A Machine Learning Library for Time Series [73.46386700728577]
  Merlion is an open-source machine learning library for time series. It features a unified interface for models and datasets for anomaly detection and forecasting. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a model in production.
  arXiv  Detail & Related papers  (2021-09-20T02:03:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.