This paper presents an AI-powered spatio-temporal drift detection system capable of tracking real-time data streams across multiple domains, including space, environmental, and biological data. The system processes data from external sources and constructs a dynamic baseline of normal behavior using historical time-series records. A hybrid drift detection mechanism is employed, integrating statistical techniques such as z-score analysis, distribution shift estimation, and adaptive window-based change detection (ADWIN) to detect both abrupt and gradual changes in data distributions. This enables the system to identify data drift, estimate its severity, and analyze trends in streaming data. In addition, the system incorporates an outlier correction mechanism that constrains extreme values within statistically acceptable limits, improving data consistency and stability. The implementation utilizes a FastAPI-based backend and an interactive visualization frontend for efficient real-time monitoring and analysis. Experimental observations demonstrate that the proposed system effectively interprets complex patterns in multi-domain environments.
Ramesh et al. (Wed,) studied this question.
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