TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
Building sensors are embedded in physical topology, spatial hierarchy, and operational context, yet existing forecasters often treat them as isolated time series or rely on fixed covariate sets. We present TopoBrick, a training-free framework for zero-shot building IoT (Internet-of-Things) forecasting. TopoBrick uses building knowledge graphs to construct a compact structural skeleton and employs an agentic topology sampler to select target-specific exogenous variables. The selected variables are organized by deployment-time availability, separating past-known sensor states from future-known c
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- PossiblePossibly related (embedding) · 52%TopoPrimer: The Missing Topological Context in Forecasting Models - Apple Machine Learning Research →
- PossiblePossibly related (embedding) · 51%OpenSTEF/openstef →
- PossiblePossibly related (embedding) · 46%superlinked/sie →
- LinkedLinked via arxiv author · 85%Xiachong Lin →
“TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting”
- LinkedLinked via arxiv author · 85%Du Yin →
“TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting”
- LinkedLinked via arxiv author · 85%Arian Prabowo →
“TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting”
- LinkedLinked via arxiv author · 85%Hao Xue →
“TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting”
- LinkedLinked via arxiv author · 85%Wen Hu →
“TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting”