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

TL;DR

SDForger introduces a novel framework that leverages large language models to generate high-quality synthetic multivariate time series data by transforming signals into text-based embeddings, enabling flexible, efficient, and statistically faithful data synthesis.

Contribution

The paper presents a new method for time series generation using LLMs with a compact data representation, outperforming existing models and enabling multimodal integration.

Findings

Outperforms existing generative models in similarity and forecasting tasks.

Efficiently generates high-quality synthetic data from few samples.

Enables textual conditioning for multimodal time series modeling.

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.