| Summary: | Precision beekeeping combines technology and statistics aimed at managing an apiary effectively and reducing the risk of situations that can lead to bee population losses. Databases of the we4bee project of three sensorised beehives were considered for analysis. They contain interior sensor data (temperature, relative humidity, and weight) and data of meteorological events. Static and dynamic vector autoregressive models and linear and nonlinear regression models were constructed to predict the hives' internal variables. They were compared by 100-fold cross-validation adapted for time series. In general, the dynamic vector autoregressive model provided the best predictions, with a feasible computational cost. Only in some specific cases did the static vector autoregressive version produces smaller errors, although the differences were not statistically significant. Generalised additive and dynamic linear models always provided less accurate results than the dynamic vector autoregressive model. There is a need of integrating accurate predictive models, such as the dynamic vector autoregressive one. This predictive model can be integrated into a decision support system to alert the beekeeper of out-of-the-ordinary situations in the hives, and thus aid in their efficient management. © 2022 The Author(s)
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