AI RESEARCH
MSDformer: Multi-scale Discrete Transformer For Time Series Generation
arXiv CS.LG
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ArXi:2505.14202v2 Announce Type: replace Discrete Token Modeling (DTM), which employs vector quantization techniques, has nstrated 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.