基于轨迹的条件状态类比预测：多元概率预测的非参数框架
Conditional Regime Analog Forecasting with Trajectories: A Nonparametric Framework for Multivariate Probabilistic
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中文总结 AI 辅助
本研究提出多元概率时间序列预测的非参数框架CRAFT，通过构建轨迹轮廓、学习状态标签等方法保留依赖关系，经模拟基准验证其预测性能优于多种对比方法。
中文摘要 AI 辅助
我们提出了基于轨迹的条件状态类比预测（CRAFT），这是一种用于多元概率时间序列预测的非参数框架。该方法从累积多期收益中构建成对的后向和前向轨迹轮廓，通过奇异值分解、变点分割和分段聚类在两个空间中学习循环低维状态标签，并估计后向到前向的条件状态对应关系。通过结合未来状态对应关系与当前后向轨迹轮廓相似性的复合兼容性得分，对历史实现的未来轨迹轮廓进行采样以获得预测分布。与参数化向量自回归或高斯状态空间模型不同，CRAFT通过重采样完整的未来路径保留了经验横截面和多期依赖关系。我们描述了该估计量、其诊断方法以及可复现的模拟基准，该基准将CRAFT与直接类比重采样、SVD类比、无条件自助法、OLS VAR自助法和随机森林预测进行了比较。
英文摘要
We propose Conditional Regime Analog Forecasting with Trajectories (CRAFT), a nonparametric framework for multivariate probabilistic time-series prediction. The method constructs paired backward and forward trajectory profiles from cumulative multi-horizon returns, learns recurrent low-dimensional regime labels in both spaces using singular value decomposition, change-point segmentation, and segment clustering, and estimates a backward-to-forward conditional regime correspondence. Forecast distributions are obtained by sampling historically realized future trajectory profiles according to a composite compatibility score that combines future-regime correspondence with similarity to the current backward trajectory profile. Unlike parametric vector autoregressions or Gaussian state-space models, CRAFT preserves empirical cross-sectional and multi-horizon dependence by resampling complete future paths. We describe the estimator, its diagnostics, and a reproducible simulation benchmark comparing CRAFT with direct analog resampling, SVD analogs, unconditional bootstrap, OLS VAR bootstrap, and random-forest forecasts.
