mlsanityData to 7 Oct 2026

QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting

arXiv 2607.02632 ↗First posted 2 Jul 2026cs.LG cs.AIcited by 0

On the map: State-space sequence models (S4, Mamba) → Time series forecasting with SSMs

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Time series forecasting with SSMs, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 12 of its 17 references that are in the map. First posted on arXiv 2 Jul 2026.

Builds on

12 of 17 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  5. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  6. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  7. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  8. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  9. 2024Chronos: Learning the Language of Time Seriescited by 712
  10. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  11. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  12. 2020Deep Learning for Time Series Forecasting: Tutorial and Literature Surveycited by 117

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  8. 2024TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting