Adaptive Temporal Windowing for Streaming Outlier Detection Under Dynamic Arrival Rates

Streaming outlier detection requires adaptive mechanisms capable of handling continuously evolving data streams under dynamic arrival rates. Existing count-based approaches fail under bursty and irregular stream arrival patterns commonly observed in real-world systems, since they trigger model updates after a fixed number of instances without considering temporal dynamics. In this study, we propose an adaptive time-driven windowing framework for streaming outlier detection that de-couples model updates from instance count and instead leverages elapsed time as the primary control mechanism. The proposed approach is based on the Density Incremental Local Outlier Factor (DILOF) and introduces a time-aware update strategy aligned with real-world streaming behavior. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves robust and stable detection performance, with AUC values ranging up to 0.96. The results further show that time-based windowing provides a consistent trade-off between detection accuracy and computational efficiency, while offering a temporally grounded update mechanism for streams with variable arrival behavior. In addition, we analyze hybrid count-time strategies and demonstrate their limitations due to dominance effects. Repeated runs further indicate the robustness and consistency of the proposed framework. The findings highlight that temporal awareness is a critical factor in stream outlier detection and should be explicitly incorporated into windowing mechanisms, particularly in resource-constrained environments such as fog and edge computing.