[2505.14202] MSDformer: Multi-scale Discrete Transformer For Time Series Generation

Computer Science > Machine Learning arXiv:2505.14202 (cs) [Submitted on 20 May 2025 (v1), last revised 6 Apr 2026 (this version, v3)] Title:MSDformer: Multi-scale Discrete Transformer For Time Series Generation Authors:Shibo Feng, Zhicheng Chen, Xi Xiao, Zhong Zhang, Qing Li, Xingyu Gao, Peilin Zhao View a PDF of the paper titled MSDformer: Multi-scale Discrete Transformer For Time Series Generation, by Shibo Feng and 6 other authors View PDF HTML (experimental) Abstract:Discrete Token Modeling (DTM), which employs vector quantization techniques, has demonstrated remarkable success in modeling non-natural language modalities, particularly in time series generation. While our prior work SDformer established the first DTM-based framework to achieve state-of-the-art performance in this domain, two critical limitations persist in existing DTM approaches: 1) their inability to capture multi-scale temporal patterns inherent to complex time series data, and 2) the absence of theoretical foundations to guide model optimization. To address these challenges, we proposes a novel multi-scale DTM-based time series generation method, called Multi-Scale Discrete Transformer (MSDformer). MSDformer employs a multi-scale time series tokenizer to learn discrete token representations at multiple scales, which jointly characterize the complex nature of time series data. Subsequently, MSDformer applies a multi-scale autoregressive token modeling technique to capture the multi-scale patterns of ...