arXiv：cs.LG· Imane Hocine, Asma Abboura, Soror Sahri, Abhijith Senthilkumar, Yacine Hakimi, Gr\'egoire Danoy·· 3 小时前AI 评分31
TSGuard：面向流式时间序列缺失数据的实时检测与插补框架
TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series
AI 导读
TSGuard 是一个实时演示系统，用于监控、验证和插补流式时间序列中的缺失值，结合轻量级图感知时序插补模型与约束感知验证、回退估计和面向操作员的解释。该系统将插补融入数据质量闭环：检测异常观测、插补缺失值、依据物理与空间约束验证估计值，并决定保留原值作为合理异常还是替换违规值。该成果发表于 CIKM '26，以环境传感为演示场景，支持用户实时检查延迟传感器、比较插补器、定义约束并验证标记值。
正文
Abstract:Streaming sensor applications routinely suffer from delayed or missing observations caused by faults, communication losses, or environmental interference. Although recent imputation methods exploit temporal and spatial dependencies effectively, most either assume offline access to future observations or prioritize throughput without enforcing domain plausibility. We present TSGuard, a real-time demonstration system for monitoring, validating, and imputing missing values in streaming time series. TSGuard combines a lightweight graph-aware temporal imputation model with constraint-aware validation, fallback estimation, and operator-facing explanations.
Rather than treating imputation as an isolated prediction task, TSGuard integrates it into a broader data-quality loop: detect problematic observations, impute missing values, validate estimated against physical and spatial constraints, and either retain the original value as a plausible anomaly or replace it when it violates domain constraints. Using environmental sensing as a motivating setting, the demo enables users to inspect delayed sensors, compare imputers, define constraints, and validate flagged values in real time. The combination of lightweight online spatiotemporal imputation, domain-aware validation, and explicit retain-or-replace decisions is our central contribution, while interactive explanations make these decisions inspectable and actionable. for operators.
| Comments: | The 35th ACM International Conference on Information and Knowledge Management (CIKM '26), November 07--11, 2026, Rome, Italy |
| Subjects: | Databases (cs.DB); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.03147 [cs.DB] |
| (or arXiv:2610.03147v1 [cs.DB] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03147 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM '26), November 07--11, 2026, Rome, Italy |
| Related DOI: | https://doi.org/10.1145/3799682.3840280
DOI(s) linking to related resources |
Submission history
From: Soror Sahri [view email]
[v1]
Fri, 2 Oct 2026 11:18:04 UTC (2,078 KB)
来源：arXiv：cs.LG · arxiv.org