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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.04973 |
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| _version_ | 1866911253445738496 |
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| author | Li, Siyuan Sun, Yifan Cheng, Lei Wang, Lewen Liu, Yang Liu, Weiqing Li, Jianlong Bian, Jiang Fang, Shikai |
| author_facet | Li, Siyuan Sun, Yifan Cheng, Lei Wang, Lewen Liu, Yang Liu, Weiqing Li, Jianlong Bian, Jiang Fang, Shikai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04973 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Less Is More: Generating Time Series with LLaMA-Style Autoregression in Simple Factorized Latent Spaces Li, Siyuan Sun, Yifan Cheng, Lei Wang, Lewen Liu, Yang Liu, Weiqing Li, Jianlong Bian, Jiang Fang, Shikai Machine Learning 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. |
| title | Less Is More: Generating Time Series with LLaMA-Style Autoregression in Simple Factorized Latent Spaces |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.04973 |