Compositional time series frequently exhibit structural breaks due to external shocks, policy changes, or market disruptions. Standard methods either ignore such breaks or handle them through fixed effects that cannot extrapolate beyond the sample, or step-function dummies that impose instantaneous adjustment. We develop a Bayesian Dirichlet ARMA model augmented with a directional-shift intervention mechanism that captures structural breaks through three interpretable parameters: a direction vector specifying which components gain or lose share, an amplitude controlling redistribution magnitude, and a logistic gate governing transition timing and speed. The model preserves compositional constraints by construction, maintains DARMA dynamics for short-run dependence, and produces coherent probabilistic forecasts through and after structural breaks. The intervention trajectory corresponds to geodesic motion on the simplex and is invariant to the choice of ILR basis. A simulation study with 400 fits across 8 scenarios shows near-zero amplitude bias and nominal 80\% credible interval coverage when the shift direction is correctly identified (77.5\% of cases); supplementary studies confirm robustness across extreme transition speeds and non-monotone DGPs. Two empirical applications to COVID-era Airbnb data characterize performance relative to simpler alternatives. Where the break is monotone and ongoing, the intervention model achieves near-nominal calibration (79.6\%) while the fixed effect substantially under-covers (66.1\%). Where post-break dynamics are non-monotone, both models are acceptably calibrated and the fixed effect outperforms on point accuracy. The intervention model's advantages are thus specific to settings with roughly monotone structural transitions.

翻译：组合时间序列常因外部冲击、政策变化或市场扰动而呈现结构性断点。现有方法要么忽略此类断点，要么通过无法外推至样本外的固定效应或强制瞬时调整的阶跃函数哑变量进行处理。我们提出一种贝叶斯狄利克雷ARMA模型，通过引入方向偏移干预机制，利用三个可解释参数捕捉结构性断点：指定成分增减份额的方向向量、控制再分配幅度的振幅参数、以及调控过渡时机与速度的逻辑门控函数。该模型通过构造保持组合约束，保留短期依赖的DARMA动态特性，并在结构性断点期间及之后生成一致的概率预测。干预轨迹对应单纯形上的测地线运动，且对ILR基的选择具有不变性。覆盖8种场景的400次拟合模拟研究显示，当偏移方向被正确识别时（占77.5%的案例），振幅偏差趋近于零且80%置信区间覆盖率达到标称水平；补充研究验证了其在极端过渡速度与非单调DGP下的稳健性。两项针对新冠疫情时期Airbnb数据的实证分析，刻画了模型相较于简化替代方法的性能表现：当断点呈单调持续特征时，干预模型实现近标称校准率（79.6%），而固定效应严重低估覆盖（66.1%）；当断点后动态呈非单调特征时，两种模型校准性均可接受，且固定效应在点预测精度上更优。因此，干预模型的优势主要体现在呈近似单调结构过渡的场景中。