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Event-pattern complexity can drastically affect model performance, with some neural STPPs collapsing under pressure while others remain robust.
Contextual embeddings can dramatically enhance predictive accuracy in spatio-temporal forecasting, especially when historical data is sparse.
HawkesNest reveals that even structurally aligned models can falter dramatically under complex spatiotemporal conditions, exposing critical vulnerabilities in STPP evaluations.
