---
title: "QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting — Stuff That Spins"
description: "arXiv:2607.02632v1 Announce Type: new Abstract: Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial m…"
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date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:05:00.857236+00:00"
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# QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.02632  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.02632v1 Announce Type: new 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

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