# TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting

arXiv: https://arxiv.org/abs/2607.06349 · HTML: https://mlsanity.com/paper/2607.06349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 14 of 14)

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- [TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting](https://mlsanity.com/paper/2510.00461/) (2025)
- [BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics](https://mlsanity.com/paper/2406.08990/) (2024)
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- [LightLLM: A Versatile Large Language Model for Predictive Light Sensing](https://mlsanity.com/paper/2411.15211/) (2024)
- [Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning](https://mlsanity.com/paper/2309.04296/) (2023)
- [Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned](https://mlsanity.com/paper/2406.12709/) (2024)
- [BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics](https://mlsanity.com/paper/2412.14175/) (2024)

## Abstract (arXiv)

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 calendar, schedule, and meteorological exogenous variables. Across three real-world buildings, TopoBrick outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models. Ablations show that topology-aware sampling is more reliable than random, ontology-only, or fixed-hop selection, especially for physically coupled HVAC and weather-driven sensing variables.