用离散隐空间的自回归模型，快速生成任意长度多变量时间序列。
Less Is More: Generating Time Series with LLaMA-Style Autoregression in Simple Factorized Latent Spaces
- 将时序分解为静态基底与动态系数，离散化后用自回归变换器建模
- 生成速度比扩散模型快多个数量级，仍保留通道间相关性
- 适合需要快速生成、可解释性强的时序数据场景
多变量时间序列生成对数据增强、模拟和隐私保护至关重要，但当前基于扩散的方法生成缓慢且仅支持固定长度。我们提出FAR-TS框架，通过解耦因子分解与离散量化隐空间中的自回归Transformer，实现高效生成。每个时间序列被分解为捕捉静态跨通道相关性的自适应基底，以及向量量化为离散标记的时序系数。采用类似LLaMA的自回归Transformer建模这些标记序列，实现任意长度、快速且可控的时序生成。得益于简洁设计，FAR-TS生成速度较Diffusion-TS快多个数量级，同时保持通道间相关性和可解释隐空间，实现高质量、灵活的时序合成。
原文摘要 · Abstract (English)
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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