WIRED：面向概率多序列预测的带结构化依赖的加权自适应预测
WIRED: Weighted Adaptive Prediction with Structured Dependence for Probabilistic Multiseries Forecasting
AI总结：
本文提出用于多相关时间序列联合概率预测的R包算法WIRED，其分离自适应边际专家聚合与依赖重建，经基准测试发现CRPS外推的softmax权重存在瓶颈，为正则化概率集成构建提供研究路径。
AI中文摘要：
本文提出了WIRED，这是一个用于多个相关时间序列联合概率预测的R包算法。WIRED结合了简单边际预测分布库、基于连续排名概率得分（CRPS）的自适应混合权重，以及用于跨序列模拟的高斯或学生t copula。我们在包含四个合成数据生成过程（DGP）、三个预测时域、每个DGP-时域对30次重复、九项 ablation 研究及外部基准的基准测试中评估该实现，并对内置的EuStockMarkets数据开展滚动起点研究。核心贡献在于架构与诊断层面：WIRED将自适应边际专家聚合与依赖重建分离；该基准支持显式依赖建模，但显示当前CRPS外推的softmax权重尚未足够鲁棒，无法优于更简单的自助法或等权重替代方案。因此，本文明确了设计的工作层、边际聚合层的瓶颈，以及构建更具正则化的概率集成的具体研究路径。
英文摘要：
This paper presents WIRED, an R package algorithm for joint probabilistic forecasting of multiple related time series. WIRED combines a library of simple marginal predictive distributions, CRPS-based adaptive mixture weights, and a Gaussian or Student t copula for cross-series simulation. We evaluate the implementation in a benchmark with four synthetic data-generating processes (DGPs), three forecast horizons, 30 replicates per DGP-horizon pair, nine ablations and external baselines, and a rolling-origin study on the built-in EuStockMarkets data. The central contribution is architectural and diagnostic. WIRED separates adaptive marginal expert aggregation from dependence reconstruction; the benchmark supports explicit dependence modeling, but shows that the current CRPS-extrapolated softmax weighting is not yet robust enough to dominate simpler bootstrap or equal-weight alternatives. The paper therefore identifies a working layer of the design, a bottleneck in the marginal aggregation layer, and a concrete research path for more regularized probabilistic ensemble construction.
