Less Is More: Generating Time Series with LLaMA-Style Autoregression in Simple Factorized Latent Spaces

• URL: http://arxiv.org/abs/2511.04973v1
• Date: Fri, 07 Nov 2025 04:15:38 GMT
• Title: Less Is More: Generating Time Series with LLaMA-Style Autoregression in Simple Factorized Latent Spaces
• Authors: Siyuan Li, Yifan Sun, Lei Cheng, Lewen Wang, Yang Liu, Weiqing Liu, Jianlong Li, Jiang Bian, Shikai Fang,
• Abstract summary: We propose FAR-TS, a simple yet effective framework that combines disentangled factorization with an autoregressive Transformer over a discrete, quantized latent space to generate time series.<n>A LLaMA-style autoregressive Transformer then models these token sequences, enabling fast and controllable generation of sequences with arbitrary length.
• Score: 29.349850914912846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models for multivariate time series are essential for data augmentation, simulation, and privacy preservation, yet current state-of-the-art diffusion-based approaches are slow and limited to fixed-length windows. We propose FAR-TS, a simple yet effective framework that combines disentangled factorization with an autoregressive Transformer over a discrete, quantized latent space to generate time series. Each time series is decomposed into a data-adaptive basis that captures static cross-channel correlations and temporal coefficients that are vector-quantized into discrete tokens. A LLaMA-style autoregressive Transformer then models these token sequences, enabling fast and controllable generation of sequences with arbitrary length. Owing to its streamlined design, FAR-TS achieves orders-of-magnitude faster generation than Diffusion-TS while preserving cross-channel correlations and an interpretable latent space, enabling high-quality and flexible time series synthesis.

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