QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting
QuantFlow is a probabilistic forecasting framework that combines inverted sequence embedding, bidirectional Mamba state-space decoders, quantile regression, and federated learning. Experiments demonstrate strong performance across multiple datasets and effective accuracy retention in non-IID federated settings, while also revealing limitations on irregular epidemiological signals and long-horizon generalization.
-->
[Submitted on 2 Jul 2026]
Title:QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting
View a PDF of the paper titled QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting, by Shah Nawaz Haider and 4 other authors
View PDF
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.
Comments: 7 pages, 4 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
ACM classes: I.2.6
Cite as: arXiv:2607.02632 [cs.LG]
(or arXiv:2607.02632v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.02632
arXiv-issued DOI via DataCite
Submission history
From: Shah Nawaz Haider [view email] [v1] Thu, 2 Jul 2026 14:16:48 UTC (454 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting, by Shah Nawaz Haider and 4 other authors
View PDF
view license
Current browse context:
cs.LG
new | recent | 2026-07
Change to browse by:
cs cs.AI
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)