QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting
Summary
QuantFlow introduces a federated Mamba-based foundation model for time-series forecasting that combines inverted sequence embedding, bidirectional state-space decoders, and quantile regression to achieve strong results on benchmarks while preserving data privacy.
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# QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting Source: [https://arxiv.org/abs/2607.02632](https://arxiv.org/abs/2607.02632) [View PDF](https://arxiv.org/pdf/2607.02632) > Abstract:Time\-series forecasting supports decisions in finance, en\-ergy, transportation, public health, and industrial monitoring\. Recent foundation models improve transfer across forecast\-ing tasks, but many depend on centralized data and Trans\-former attention, which restricts their use for long, high\-di\-mensional, and privacy\-sensitive signals\. This paper presents QuantFlow, a probabilistic forecasting framework that com\-bines inverted sequence embedding, bidirectional Mamba state\-space decoders, quantile regression, and federated learning\. Each variable is embedded over the complete ob\-servation window, processed in forward and reverse direc\-tions, and projected to five conditional quantiles\. TSMixup expands temporal diversity through Dirichlet\-weighted inter\-polation while preserving sequence structure\. Experiments cover cryptocurrency, traffic, electricity, Electricity Trans\-former Temperature, influenza, and weather data\. QuantFlow obtains mean squared errors of 0\.2834 on ETTm1 and 0\.2218 on Weather, and a 20\-client non\-IID deployment retains use\-ful accuracy after three communication rounds without cen\-tralizing raw records\. The results indicate that selective state\-space modelling is a promising basis for scalable, uncer\-tainty\-aware, and privacy\-conscious time\-series prediction, while also revealing limitations on irregular epidemiological signals and long\-horizon generalization\. ## Submission history From: Shah Nawaz Haider \[[view email](https://arxiv.org/show-email/da556a1f/2607.02632)\] **\[v1\]**Thu, 2 Jul 2026 14:16:48 UTC \(454 KB\)
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