Multivariate Time-Series Forecasting of Beehive Microclimate Parameters Using Bayesian Vector Autoregression
Received: 31 December 2025 | Revised: 4 February 2026 | Accepted: 11 February 2026 | Online: 4 April 2026
Corresponding author: Punith Kumar
Abstract
Enhancing precision in beekeeping involves integrating technology and statistical models to assess honeybee health and mitigate the risk of colony loss. Monitoring the rate of hive population decline is a crucial tool for bee health management, providing early warnings of potential abnormalities affecting colonies. Through the we4bee project, data on humidity, temperature, and weight were collected using interior sensors placed inside the beehive. These datasets were analyzed to predict internal hive variables using Vector Autoregressive (VAR) and statistical models. This work introduces a Bayesian VAR (BVAR) model, a new framework that applies Bayesian statistics to improve the VAR model by integrating prior information, managing uncertainty, and enhancing parameter estimation. A comparative analysis of time-series data, performed with 75-fold cross-validation, showed that the new BVAR model yielded the most accurate predictions and required less computation time. Given the need for accurate predictive models, these approaches could help beekeepers prevent hive collapse and improve overall hive management.
Keywords:
Bayesian inference, vector autoregression, beehive microclimate, rolling window approachDownloads
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