发表机构
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多元非平稳时间序列预测与不确定性量化问题，提出ABF-T-GLCP框架，通过学习自适应预测状态表示，结合预测与共形校准模块，实验表明该框架能提升预测准确性、缩小区间，且应用范围广。
AI 中文摘要
我们提出了ABF-T-GLCP，这是一个用于非平稳多元时间序列预测和不确定性量化的模型无关框架。核心思想是学习用于点预测的自适应预测状态表示并将其用于共形校准。预测模块通过学习的门组合特定时间范围的时间专家，并使用跨相关序列的稀疏预测转移来细化预测。不确定性模块，即门局部共形预测（GLCP），使用学习的门状态以及时间新近度来选择局部相关的校准残差，从而将不确定性校准与预测模型使用的预测机制耦合。这种共享表示允许点预测和预测区间在不断变化的时间动态下一致地适应，同时保留共形预测的模型无关性质，并在温和稳定性条件下产生近似局部覆盖。在大规模高频商品预测基准上的实验表明，点预测准确性持续提高，预测区间大幅变窄，经验覆盖接近名义水平。其他结果表明该框架超出了金融应用的范畴。
英文摘要
We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The central idea is to learn an adaptive predictive state representation for point forecasting and reuse it for conformal calibration. The forecasting module combines horizon-specific temporal experts through a learned gate and refines predictions using sparse predictive transfer across related series. The uncertainty module, Gate-Localized Conformal Prediction (GLCP), uses the learned gate state, together with temporal recency, to select locally relevant calibration residuals, thereby coupling uncertainty calibration to the predictive regimes used by the forecasting model. This shared representation allows point forecasts and prediction intervals to adapt consistently under evolving temporal dynamics while retaining the model-agnostic nature of conformal prediction and yielding approximate local coverage under mild stability conditions. Experiments on a large-scale high-frequency commodity forecasting benchmark show consistent gains in point forecasting accuracy and substantially narrower prediction intervals with empirical coverage close to the nominal level. Additional results indicate that the framework extends beyond the motivating financial application.
