发表机构
Minjiang University（闽江学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究系统评估差分隐私合成时间序列的窗口组装策略，发现下游预测效用由预测器、重叠率和加权方案共同决定，且连续性诊断不足以指导组装选择。
AI 中文摘要
差分隐私时间序列生成器通常产生固定长度的合成窗口，而下游预测模型往往需要长连续训练序列。因此，即使训练好的生成器保持不变，这些窗口在生成后的组装方式也可能改变呈现给预测器的有效合成数据。我们通过系统变化重叠率和窗口加权方案，并评估所得序列的边界连续性、统计和时间保真度以及训练于合成测试于真实（TSTR）预测效用来研究这一生成后序列组装过程。在四类公开数据集（ETTh1、ETTm1、Weather和Appliances）和五个预测模型上，结果揭示了清晰的依赖于预测器的组装原则：下游TSTR效用由预测器、重叠率和窗口加权方案共同塑造，导致不同预测模型间出现不同的组装偏好。增加重叠通常改善边界连续性，但连续性或个体保真度诊断的改善并不一致地降低预测误差，表明仅凭这些诊断不足以选择组装配置。完整的五预测器组装网格，连同匹配的训练于真实测试于真实（TRTR）参考，进一步表征了这些规律，并量化了相对于真实数据训练的组装依赖性效用。然后，我们通过额外的鲁棒性和生成器变异性分析验证了所识别的原则。
英文摘要
Differentially private time-series generators commonly produce fixed-length synthetic windows, whereas downstream forecasting models often require long continuous training sequences. How these windows are assembled after generation can therefore alter the effective synthetic data presented to a forecaster, even when the trained generator remains unchanged. We study this post-generation sequence assembly process by systematically varying overlap rates and window-weighting schemes and evaluating the resulting sequences in terms of boundary continuity, statistical and temporal fidelity, and Train-on-Synthetic-Test-on-Real (TSTR) forecasting utility. Across four types of public datasets (ETTh1, ETTm1, Weather, and Appliances) and five forecasting models, the results reveal a clear forecaster-dependent assembly principle: downstream TSTR utility is jointly shaped by the forecaster, overlap rate, and window-weighting scheme, leading to distinct assembly preferences across forecasting models. Increased overlap generally improves boundary continuity, but improvements in continuity or individual fidelity diagnostics do not consistently reduce forecasting error, indicating that these diagnostics alone are insufficient for selecting assembly configurations. Complete five-forecaster assembly grids, together with matched Train-on-Real-Test-on-Real (TRTR) references, further characterize these regularities and quantify assembly-dependent utility relative to real-data training. We then validate the identified principles through additional analyses of robustness and generator variability.
