  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 controllers;
(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
beekeepers 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 coincide 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.
