aiqbench analysis
This paper applies generative modeling to time series by combining multi-scale tokenization with categorical flow matching, which generates a single token sequence non-autoregressively instead of decoding several streams one at a time. The reported gains are in generative fidelity, cross-scale consistency, and inference speed against autoregressive and diffusion baselines, but the claims rest on four benchmarks and need independent replication. For ML readers, the useful question is whether shared-index voting and low-rank anchors generalize beyond these datasets, and code is available for checking that.
UniFlow: Unified Multi-Scale Flow Matching for Time Series Generation
arXiv:2611.04825v1 Announce Type: cross Abstract: Real-world time series exhibit complex dynamics across multiple temporal resolutions, yet effectively incorporating multi-scale information into generative modeling remains challenging without increasing generative complexity. We propose UniFlow, a unified framework that integrates multi-scale representation learning with efficient non-autoregressive generation through a shared-semantic discrete interface. Specifically, a multi-scale tokenizer employs shared-index voting to consolidate complementary temporal information into a single public-ID sequence, while scale-specific codebooks preserve the information required for reconstruction. Building on this unified representation, we develop geometry-aware categorical flow matching, which initializes transport from learned low-rank anchors and generates public identities non-autoregressively. This design captures multi-scale temporal structure without requiring multiple generative token streams or sequential token decoding. Experiments across four benchmarks and both short- and long-term settings demonstrate improved generative fidelity, strong cross-scale consistency, and substantial inference speedups over representative autoregressive and diffusion-based baselines. Code is available at https://github.com/William-Liwei/uniflow
Abstract:Real-world time series exhibit complex dynamics across multiple temporal resolutions, yet effectively incorporating multi-scale information into generative modeling remains challenging without increasing generative complexity. We propose UniFlow, a unified framework that integrates multi-scale representation learning with efficient non-autoregressive generation through a shared-semantic discrete interface. Specifically, a multi-scale tokenizer employs shared-index voting to consolidate complementary temporal information into a single public-ID sequence, while scale-specific codebooks preserve the information required for reconstruction. Building on this unified representation, we develop geometry-aware categorical flow matching, which initializes transport from learned low-rank anchors and generates public identities non-autoregressively. This design captures multi-scale temporal structure without requiring multiple generative token streams or sequential token decoding. Experiments across four benchmarks and both short- and long-term settings demonstrate improved generative fidelity, strong cross-scale consistency, and substantial inference speedups over representative autoregressive and diffusion-based baselines. Code is available at this https URL
Machine Learning (stat.ML); Artificial Intelligence (cs.AI)
arXiv:2611.04825 [stat.ML]
(or arXiv:2611.04825v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2611.04825 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 240 words
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