TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generation

• URL: http://arxiv.org/abs/2601.11184v1
• Date: Fri, 16 Jan 2026 11:00:05 GMT
• Title: TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generation
• Authors: Xiangyu Xu, Qingsong Zhong, Jilin Hu,
• Abstract summary: We propose a structure-disentangled multiscale generation framework for time series.<n>Our approach encodes sequences into discrete tokens at multiple temporal resolutions.<n>We show that our approach produces higher-quality time series than existing methods.
• Score: 11.455232661227313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative modeling offers a promising solution to data scarcity and privacy challenges in time series analysis. However, the structural complexity of time series, characterized by multi-scale temporal patterns and heterogeneous components, remains insufficiently addressed. In this work, we propose a structure-disentangled multiscale generation framework for time series. Our approach encodes sequences into discrete tokens at multiple temporal resolutions and performs autoregressive generation in a coarse-to-fine manner, thereby preserving hierarchical dependencies. To tackle structural heterogeneity, we introduce a dual-path VQ-VAE that disentangles trend and seasonal components, enabling the learning of semantically consistent latent representations. Additionally, we present a guidance-based reconstruction strategy, where coarse seasonal signals are utilized as priors to guide the reconstruction of fine-grained seasonal patterns. Experiments on six datasets show that our approach produces higher-quality time series than existing methods. Notably, our model achieves strong performance with a significantly reduced parameter count and exhibits superior capability in generating high-quality long-term sequences. Our implementation is available at https://anonymous.4open.science/r/TimeMAR-BC5B.

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