{"id":1155901,"date":"2025-11-18T08:08:57","date_gmt":"2025-11-18T16:08:57","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=1155901"},"modified":"2025-11-18T08:08:57","modified_gmt":"2025-11-18T16:08:57","slug":"less-is-more-generating-time-series-with-llama-style-autoregression-in-simple-factorized-latent-spaces","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/less-is-more-generating-time-series-with-llama-style-autoregression-in-simple-factorized-latent-spaces\/","title":{"rendered":"Less Is More: Generating Time Series with LLaMA-Style Autoregression in Simple Factorized Latent Spaces"},"content":{"rendered":"<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 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