Short-Horizon Forecasting of Indoor Mold-Favorable Conditions for Edge Early Warning: A Leakage-Controlled Benchmark of Classical and Temporal Models

Excess indoor moisture can affect occupant health, damage building materials, and create conditions favorable to mold growth. Many monitoring systems rely on fixed thresholds and current sensor readings, offering little anticipation of unfavorable conditions. This study formulates indoor mold risk monitoring as a short-horizon forecasting problem for edge-based early warning. Recent relative humidity measurements are used to predict whether the subsequent 30 or 60 min interval is Safe, Elevated, or Mold-Favorable. A residual Temporal Convolutional Network (TCN) was compared to classical machine learning models and simple baselines using a public indoor time-series dataset. To prevent temporal leakage, the training, validation, and test sets were separated chronologically by explicit gaps. At the primary 60 min horizon, the TCN achieved a mean Macro F1 of 0.7886 and performed comparably to the strongest classical models. Robustness varied by disturbance type. The selected TCN was exported to ONNX and deployed on a Raspberry Pi 5, where the complete edge pipeline passed controlled tests and a 90 min live sensor run. The three classes represent environmental conditions associated with mold risk, not actual mold presence or biological growth. The system is therefore intended as a preventive warning tool rather than a mold-detection method.