Abstract
Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting, demonstrating strong transferability and data efficiency gains. However, their adoption in financial applications is hindered by fundamental limitations in uncertainty quantification: current approaches either rely on restrictive distributional assumptions, conflate different sources of uncertainty, or lack principled calibration mechanisms. While recent TSFMs employ sophisticated techniques such as mixture models, Student's t-distributions, or conformal prediction, they fail to address the core challenge of providing theoretically-grounded uncertainty decomposition. For the very first time, we present a novel transformer-based probabilistic framework, ProbFM (probabilistic foundation model), that leverages Deep Evidential Regression (DER) to provide principled uncertainty quantification with explicit epistemic-aleatoric decomposition. Unlike existing approaches that pre-specify distributional forms or require sampling-based inference, ProbFM learns optimal uncertainty representations through higher-order evidence learning while maintaining single-pass computational efficiency. To rigorously evaluate the core DER uncertainty quantification approach independent of architectural complexity, we conduct an extensive controlled comparison study using a consistent LSTM architecture across five probabilistic methods: DER, Gaussian NLL, Student's-t NLL, Quantile Loss, and Conformal Prediction. Evaluation on cryptocurrency return forecasting demonstrates that DER maintains competitive forecasting accuracy while providing explicit epistemic-aleatoric uncertainty decomposition. This work establishes both an extensible framework for principled uncertainty quantification in foundation models and empirical evidence for DER's effectiveness in financial applications.
Abstract (translated)
时间序列基础模型（TSFMs）在零样本金融预测中表现出众，展现了强大的迁移能力和数据效率。然而，由于不确定性量化的基本限制，它们在金融应用中的采用受到阻碍：目前的方法要么依赖于严格的分布假设，混淆了不同的不确定来源，要么缺乏原则性的校准机制。尽管最近的TSFMs采用了复杂的技巧，如混合模型、Student's t-分布或符合预测，但这些方法未能解决提供基于理论的不确定性分解这一核心挑战。我们首次提出了一种新的基于转换器的概率框架——ProbFM（概率基础模型），该框架利用深度证据回归（DER）来提供具有明确知识论—偶然性分解的原则性不确定性量化。 与现有的预设分布形式或要求采样推理的方法不同，ProbFM通过更高阶的证据学习来学习最优的不确定性表示，并保持单次计算效率。为了严格评估独立于架构复杂性的核心DER不确定性量化方法，我们使用一致的LSTM架构对五种概率方法进行了广泛的受控对比研究：DER、高斯负日志似然（Gaussian NLL）、Student's t-负日志似然(Student's-t NLL)、分位数损失(Quantile Loss)和符合预测(Conformal Prediction)。在加密货币回报预测的评估中，结果表明，尽管提供明确的知识论—偶然性不确定性分解，DER仍能保持竞争力的预测准确性。 这项工作不仅为基础模型中的原则性不确定性量化建立了一个可扩展框架，还提供了DER在金融应用中的有效性的实证证据。
URL
https://arxiv.org/abs/2601.10591
