QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting
arXiv:2607.02632v1 Announce Type: cross 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 vari
The increasing demand for privacy-preserving and efficient AI models for time-series forecasting, especially with growth in decentralized data, drives the development of frameworks like QuantFlow.
This development addresses critical limitations of current foundation models, specifically around data privacy, computational efficiency for long sequences, and decentralized deployment, which are crucial for sensitive and large-scale applications.
The ability to deploy powerful AI models like Mamba in a federated learning context allows for more robust, privacy-respecting, and scalable time-series forecasting across diverse industries without centralizing sensitive data.
- · Industries with privacy-sensitive data (e.g., healthcare, finance)
- · Edge computing providers
- · Developers of Mamba-based architectures
- · Federated learning platforms
- · Centralized data analytics platforms
- · Traditional Transformer-based forecasting models in privacy-sensitive domains
- · Organizations reliant on large-scale data aggregation for time-series insights
QuantFlow enables more accurate financial, healthcare, and infrastructure predictions while maintaining data sovereignty.
This could lead to a proliferation of specialized, privacy-preserving AI agents and forecasting services operating on local data.
The enhanced privacy and efficiency of such models might accelerate the adoption of AI in highly regulated sectors, fundamentally altering operational paradigms.
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Read at arXiv cs.AI