arXiv:2505.17103v2 Announce Type: replace 
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.

إطار جديد يسمى SDForger يثير ضجة في مجال توليد البيانات الاصطناعية من خلال استخدام نماذج اللغة الكبيرة (LLMs) لإنشاء سلاسل زمنية متعددة المتغيرات عالية الجودة. تتيح هذه الطريقة المبتكرة توليد بيانات اصطناعية من عدد قليل من العينات، مما يجعلها فعالة ومرنة. من خلال تحويل الإشارات إلى تمثيلات جدولية وضبط نماذج LLM، يفتح SDForger آفاقًا مثيرة للباحثين والشركات على حد سواء، مما يعزز قدرتهم على تحليل الاتجاهات والتنبؤ بها دون الحاجة إلى بيانات حقيقية واسعة النطاق.

Un nuevo marco llamado SDForger está causando sensación en el campo de la generación de datos sintéticos al utilizar modelos de lenguaje de gran tamaño (LLMs) para crear series temporales multivariadas de alta calidad. Este enfoque innovador permite la generación de datos sintéticos a partir de solo unas pocas muestras, lo que lo hace eficiente y flexible. Al transformar señales en embeddings tabulares y ajustar los LLMs, SDForger abre emocionantes posibilidades para investigadores y empresas, mejorando su capacidad para analizar y predecir tendencias sin necesidad de grandes cantidades de datos del mundo real.

Un nouveau cadre appelé SDForger fait sensation dans le domaine de la génération de données synthétiques en utilisant des modèles de langage de grande taille (LLMs) pour créer des séries temporelles multivariées de haute qualité. Cette approche innovante permet de générer des données synthétiques à partir de quelques échantillons, ce qui la rend à la fois efficace et flexible. En transformant les signaux en embeddings tabulaires et en ajustant les LLMs, SDForger ouvre des possibilités passionnantes pour les chercheurs et les entreprises, améliorant leur capacité à analyser et à prédire des tendances sans avoir besoin de données réelles étendues.

A new framework called SDForger is making waves in the field of synthetic data generation by utilizing large language models (LLMs) to create high-quality multivariate time series. This innovative approach allows for the generation of synthetic data from just a few samples, making it both efficient and flexible. By transforming signals into tabular embeddings and fine-tuning LLMs, SDForger opens up exciting possibilities for researchers and businesses alike, enhancing their ability to analyze and predict trends without the need for extensive real-world data.

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

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

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