TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
Time-series data are inherently multiscale, spanning diverse temporal granularities from coarse trends to fine-scale dynamics. However, existing time-series generative models provide limited control over the temporal granularity of both inputs and outputs, restricting their ability to condition on user-provided coarse sketches and generate samples at a desired target granularity. To address this, we introduce TimeTok, a unified framework for Granularity-Controllable Time-Series Generation (GC-TSG), which generates time series at any target granularity from any coarser input (e.g., rough sketches) or without conditioning. At the core of TimeTok is a hierarchical tokenization strategy that ...
| Authors | Seokhyun Lee, Jaeho Kim, Changjun Oh et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 May 2026 |
| Last updated at source | 29 September 2026 |
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