具有分布自由不确定性的自适应制度切换预测：深度切换状态空间模型与适应性符合预测的结合
Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction
AI总结：
本文提出了一种结合深度切换状态空间模型与适应性符合推断的方法，以在非平稳性和模型不规范性下实现具有有限样本保证的预测带，提升预测的鲁棒性和准确性。
摘要：
制度转换经常破坏时间序列的平稳性，使校准的不确定性与点精度同样重要。我们通过将深度切换状态空间模型与自适应符合推断（ACI）及其聚合变体（AgACI）相结合，研究了适用于制度切换预测的分布自由不确定性。我们还引入了一个统一的符合包装器，建立在强大的序列基线之上，包括S4、MC-Dropout GRU、稀疏高斯过程和一个转折点局部模型，以在非平稳性和模型不规范性下生成具有有限样本边缘保证的在线预测带。在合成和真实数据集上，符合预测器在覆盖范围接近名义水平的同时，具有竞争力的准确性和普遍改进的带效率。
英文摘要：
Regime transitions routinely break stationarity in time series, making calibrated uncertainty as important as point accuracy. We study distribution-free uncertainty for regime-switching forecasting by coupling Deep Switching State Space Models with Adaptive Conformal Inference (ACI) and its aggregated variant (AgACI). We also introduce a unified conformal wrapper that sits atop strong sequence baselines including S4, MC-Dropout GRU, sparse Gaussian processes, and a change-point local model to produce online predictive bands with finite-sample marginal guarantees under nonstationarity and model misspecification. Across synthetic and real datasets, conformalized forecasters achieve near-nominal coverage with competitive accuracy and generally improved band efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
