FlowTSFM: Turning Encoder Depth into Quantile Transport
Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the final forecast is supervised and intermediate representations have no explicit predictive role. We introduce FlowTSFM, an encoder architecture that interprets depth as a recurrent transport process: a single Transformer block is iteratively applied with shared parameters, while a quantile-flow objective supervises intermediate states along a prescribed trajectory from a prior distribution toward the final forecast. The objective combines pinball forecasting loss with path-level position matching. With only 38.8M parameters, FlowTSFM achieves c...
| Authors | Bahaeddine Abdessalem, Shifeng Xie, Zehao Xiao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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