Multi-domain Anomalous Relationships in Heterogeneous Temporal Data

Abstract

The Arctic region is crucial to global climate stability. However, recent years have witnessed periods of extreme snow and ice melt, with rising temperatures that double the global average. These are not isolated events. They are the result of intricate interconnections across distinct domains. The challenge, therefore, lies not in understanding these individual domains, such as temperature, and radiation, but in decoding the inter-domain relationships inducing these polar anomalies. To address this, our study presents a novel framework aimed at mining these inter-domain relationships to explain such anomalies and the relationship across time series features comprehensively. These features may be selected from the same or different domains. Such anomalous relationships across features could help detect interesting phenomena such as extreme snow melt, and cloud cover and help identify time periods of interest when such relationships are more prevalent. We extracted the anomalous intervals in each domain using the Poisson Distribution model of rSatScan, then leveraged the concept of Direct Overlap and Proximity of anomalies to identify the direct and time-delayed temporal association (delayed correlation) between anomalies across features. The concept helps us understand how events in one domain may be associated with events in another domain during specific time periods using association rule mining. We evaluated our approach using ERA5 reanalysis data, and validated the identified anomalies against ground truth and evaluated the strength of the generated association rules using metrics like confidence and lift. Notably, several of our identified rules were consistent with findings confirmed by domain experts.