用大模型生成高质量多变量时间序列，仅需少量样本即可完成微调。
Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation
- 将时序数据转为表格嵌入，再编码成文本微调大模型。
- 生成的合成数据保留原始统计特性与时间动态，效果优于现有模型。
- 支持文本条件生成，适合需要多模态融合的时序任务。
SDForger 是一个灵活高效的框架，利用大语言模型（LLM）生成高质量多变量时间序列。通过紧凑的数据表示，该框架仅需少量样本和低计算量的微调，即可对任意自回归大模型进行适配。具体而言，框架将单变量与多变量信号转换为表格嵌入，并编码为文本，用于微调大模型。推理阶段，从文本嵌入中采样并解码生成合成时间序列，其保持了原始数据的统计特性与时间动态。在多种数据集上，SDForger 在相似性评估和下游预测任务中均优于现有生成模型。通过实现生成过程中的文本条件化，该方法为多模态建模及时间序列与文本信息的无缝集成铺平道路。代码已开源：https://github.com/IBM/fms-dgt/tree/main/fms_dgt/public/databuilders/time_series。
原文摘要 · Abstract (English)
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.
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