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Honeybees, as natural crop pollinators, play a significant role in biodiversity and food production for human civilization. Bees actively regulate hive temperature (homeostasis) to maintain a colony’s proper functionality. Deviations from usual thermoregulation behavior due to external stressors (e.g., extreme environmental temperature, parasites, pesticide exposure) indicate an impending colony collapse. Anticipating such threats by forecasting hive temperature and finding changes in temperature patterns would allow beekeepers to take early preventive measures and avoid critical issues. In that case, how can we model bees’ thermoregulation behavior for an interpretable and effective hive monitoring system? In this article, we propose theprincipledElectronic Bee-Veterinarian Plus (EBV+) method based on the thermal diffusion equation and a novel “sigmoid” feedback-loop (P) controller for analyzing hive health with the following properties: (i) it iseffectiveon multiple, real-world beehive time sequences (recorded and streaming), (ii) it isexplainablewith only a few parameters (e.g., hive health factor) that beekeepers can easily quantify and trust, (iii) it issuesproactivealerts to beekeepers before any potential issue affecting homeostasis becomes detrimental, and (iv) it isscalablewith a time complexity of\(O(t)\)for reconstructing and\(O(t\times m)\)for findingmcuts of a sequence withttime-ticks. Experimental results on multiple real-world time sequences showcase the potential and practical feasibility of EBV+. Our method yields accurate forecasting (up to72%improvement in RMSE) with up to600times fewer parameters compared to baselines (ARX, seasonal ARX, Holt-winters, and DeepAR), as well as detects discontinuities and raises alerts that coincide with domain experts’ opinions. Moreover, EBV+ is scalable and fast, taking less than1 minuteon a stock laptop to reconstruct 2 months of sensor data. 

Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else? We propose the EBV (Electronic Bee-Veterinarian) method, which has the following desirable properties: (i) principled: it is based on a) diffusion equations from physics and b) control theory for feedback-loop con- trollers; (ii) effective: it works well on multiple, real- world time sequences, (iii) explainable: it needs only a handful of parameters (e.g., bee strength) that beekeep- ers can easily understand and trust, and (iv) scalable: it performs linearly in time. We applied our method to multiple real-world time sequences, and found that it yields accurate forecasting (up to 49% improvement in RMSE compared to baselines), and segmentation. Specifically, discontinuities detected by EBV mostly co- incide with domain expert’s opinions, showcasing our approach’s potential and practical feasibility. Moreover, EBV is scalable and fast, taking about 20 minutes on a stock laptop for reconstructing two months of sensor data.