基于自注意力的扩散模型用于局部停电场景下的时间序列插补
Self-attention-based Diffusion Model for Time-series Imputation in Partial Blackout Scenarios
AI总结：
本文针对多元时间序列中的“局部停电”缺失模式，提出了一种基于自注意力和扩散过程的两阶段插补模型，有效处理训练中的缺失数据，并在真实数据集上展现出优于现有方法的性能和可扩展性。
摘要：
多元时间序列数据中的缺失值会损害机器学习性能并引入偏差。这些数据缺口由传感器故障、停电和人为错误引起，通常通过数据插补来解决。先前的工作已经解决了随机、完全停电和预测场景下缺失数据的插补问题。本文解决了一种更一般的缺失模式，我们称之为“局部停电”，即在连续的时间步中缺失部分特征。我们引入了一种两阶段插补过程，使用自注意力和扩散过程来建模特征和时间相关性。值得注意的是，我们的模型在训练期间有效地处理缺失数据，增强了适应性，并确保了可靠的插补和性能，即使在不完整的数据集上也是如此。我们在基准和两个真实世界时间序列数据集上的实验表明，我们的模型在局部停电场景中优于最先进的方法，并显示出更好的可扩展性。
英文摘要：
Missing values in multivariate time series data can harm machine learning performance and introduce bias. These gaps arise from sensor malfunctions, blackouts, and human error and are typically addressed by data imputation. Previous work has tackled the imputation of missing data in random, complete blackouts and forecasting scenarios. The current paper addresses a more general missing pattern, which we call "partial blackout," where a subset of features is missing for consecutive time steps. We introduce a two-stage imputation process using self-attention and diffusion processes to model feature and temporal correlations. Notably, our model effectively handles missing data during training, enhancing adaptability and ensuring reliable imputation and performance, even with incomplete datasets. Our experiments on benchmark and two real-world time series datasets demonstrate that our model outperforms the state-of-the-art in partial blackout scenarios and shows better scalability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
