发表机构
Department of Computer Science and Engineering, University of Science and Technology Chittagong; Department of Electrical and Electronic Engineering, University of Science and Technology Chittagong; Faculty of Science, Engineering and Technology, University of Science and Technology Chittagong（信息科学与工程系，查塔姆冈科技大学; 电气电子工程系，查塔姆冈科技大学; 科学、工程与技术学院，查塔姆冈科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对时间序列预测，提出结合多种技术的概率预测框架QuantFlow，用反向序列嵌入等进行处理，经实验验证其在多数据上效果好，且联合学习可保护隐私。
AI 中文摘要
时间序列预测支持金融、能源等领域决策。近期基础模型改善跨预测任务迁移，但依赖集中数据和Transformer注意力。本文提出QuantFlow，结合反向序列嵌入、双向曼巴状态空间解码器、分位数回归和联合学习，经实验验证其效果及隐私保护优势，也揭示了局限性。
英文摘要
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.
Comments9 pages, 4 figures
