Compositional time series, vectors of proportions summing to unity observed over time, frequently exhibit structural breaks due to external shocks, policy changes, or market disruptions. Standard methods either ignore such breaks or handle them through ad-hoc dummy variables that cannot extrapolate beyond the estimation sample. We develop a Bayesian Dirichlet ARMA model augmented with a directional-shift intervention mechanism that captures structural breaks through three interpretable parameters: a unit direction vector specifying which components gain or lose share, an amplitude controlling the magnitude of redistribution, and a logistic gate governing the timing and speed of transition. The model preserves compositional constraints by construction, maintains innovation-form DARMA dynamics for short-run dependence, and produces coherent probabilistic forecasts during and after structural breaks. We establish that the directional shift corresponds to geodesic motion on the simplex and is invariant to the choice of ILR basis. A comprehensive simulation study with 400 fits across 8 scenarios demonstrates that when the shift direction is correctly identified (77.5% of cases), amplitude and timing parameters are recovered with near-zero bias, and credible intervals for the mean composition achieve nominal 80% coverage; we address the sign identification challenge through a hemisphere constraint. An empirical application to fee recognition lead-time distributions during COVID-19 compares baseline, fixed-effects, and intervention specifications in rolling forecast evaluation, demonstrating the intervention model's superior point accuracy (Aitchison distance 0.83 vs. 0.90) and calibration (87% vs. 71% coverage) during structural transitions.

翻译：组合时间序列（即随时间观测、各分量比例之和恒为一的比例向量）常因外部冲击、政策变动或市场中断而呈现结构突变。传统方法或忽略此类突变，或通过无法外推至估计样本之外的临时虚拟变量进行处理。本文提出一种增强方向性偏移干预机制的贝叶斯狄利克雷ARMA模型，该机制通过三个可解释参数捕捉结构突变：指定哪些组分获得或损失份额的单位方向向量、控制再分配幅度的振幅参数，以及调控转变时机与速度的逻辑门函数。该模型在构造上保持组合约束，维持创新形式DARMA动态以刻画短期依赖关系，并能在结构突变期间及之后生成协调的概率预测。我们证明方向性偏移对应于单纯形上的测地运动，且对ILR基的选择具有不变性。通过涵盖8种场景、总计400次拟合的综合仿真研究表明：当偏移方向被正确识别时（77.5%的案例），振幅与时机参数的估计偏差接近零，且平均组合的置信区间达到名义80%的覆盖水平；我们通过半球约束解决了符号识别难题。在COVID-19期间费用确认前置时间分布的实证应用中，通过滚动预测评估比较了基线模型、固定效应模型与干预模型的性能，结果表明干预模型在结构转变期间具有更优的点预测精度（Aitchison距离0.83对比0.90）与校准能力（87%对比71%的区间覆盖）。