TL;DR
MSDformer introduces a multi-scale discrete transformer approach for time series generation, effectively capturing complex patterns and providing theoretical validation, leading to superior performance over existing methods.
Contribution
The paper presents a novel multi-scale DTM-based method, MSDformer, with a multi-scale tokenizer and modeling technique, supported by theoretical analysis and improved experimental results.
Findings
MSDformer outperforms state-of-the-art methods in time series generation.
Theoretical validation via rate-distortion theorem supports the approach.
Multi-scale modeling enhances the quality of generated time series.
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.…
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